Diagnostic and prognostic research最新文献

{"title":"Natriuretic peptides testing and survival prediction models for chronic heart failure: a systematic review of added prognostic value.","authors":"Charlotte A Smith, Kathryn S Taylor, Nicholas R Jones, Dominik Roth, Amy Magona, Nia Roberts, Clare J Taylor, F D Richard Hobbs, Maria D L A Vazquez-Montes","doi":"10.1186/s41512-025-00210-x","DOIUrl":"10.1186/s41512-025-00210-x","url":null,"abstract":"<p><strong>Background: </strong>High natriuretic peptide levels are associated with a poor outcome in adults with chronic heart failure (CHF). However, the incremented prediction accuracy of multivariable prognostic models after adding B-type natriuretic peptide (BNP) and/or N-terminal proBNP (NT-proBNP) remains unclear.</p><p><strong>Methods: </strong>We carried out a systematic review narrative analysis of added-value studies of BNP and NT-proBNP in CHF prognostication. Primary clinical studies investigating prognostic model development or validation in adult participants with CHF were included. Any studies of individual factors' association with patient outcomes, treatment efficacy, or those using patients with transplant/ventricular assist devices, ≥ 10% of patients with advanced HF, or significant comorbidities, HF secondary to congenital/reversible conditions, or ≥ 33% of patients with valvular HF were excluded. The databases MEDLINE, Embase, Science Citation Index, and Cochrane Prognosis Methods Group Database were searched from January 1990 to February 2024. Predictive performance was measured in terms of discrimination and calibration, the added value in terms of the c-statistic difference before and after adding BNP and/or NT-proBNP to a base model, and the risk reclassification, namely, net reclassification index (NRI) and integrated discrimination improvement (IDI). Risk of bias assessment used the Prediction model Risk Of Bias ASsessment Tool (PROBAST).</p><p><strong>Results: </strong>Fourteen added-value studies comprising a total of 50,949 individuals were included. Both BNP and NT-proBNP consistently improved mortality prediction performance, but studies only presented separately before and after c-statistics, without formally testing for statistically significant differences. Meta-analysis was impossible due to missing data on the change in predictive performance and data heterogeneity. All studies reported discrimination. Few reported calibration, NRI, and IDI. All studies except one were deemed to be at high risk of bias, whereas 50% showed high applicability to the review question, with only 14% scoring high for applicability concern, and the rest were unclear.</p><p><strong>Conclusions: </strong>Improving consistency in researching and reporting the added value of natriuretic peptide testing to predict mortality in chronic heart failure patients could facilitate summarizing and interpreting the results more meaningfully.</p><p><strong>Registration: </strong>This review is a refinement of the methods and a search update of the review of added-value biomarkers in HF prognosis (PROSPERO registration number: CRD42019086993).</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"31"},"PeriodicalIF":2.6,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12687552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145709918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The F score ranks diagnostic tests and prediction models inconsistently with their clinical utility.","authors":"Melissa Assel, Andrew Vickers","doi":"10.1186/s41512-025-00214-7","DOIUrl":"10.1186/s41512-025-00214-7","url":null,"abstract":"<p><strong>Background: </strong>The F score is derived from precision (positive predictive value) and recall (sensitivity). It is increasingly used to evaluate diagnostic tests and prediction models in the machine learning literature. Although precision and recall can be differentially weighted using a parameter β, almost all applications use equal weighting, with β set to 1.</p><p><strong>Methods: </strong>We considered a cancer detection scenario to explore the properties of the F score in comparison to net benefit, a well-established method for evaluating the clinical utility of tests and models. Because missing cancer can be fatal and biopsies are an invasive procedure, we would favor a test with high sensitivity.</p><p><strong>Results: </strong>F scores did not provide a rank ordering of tests consistent with utility. F1 was highest for a test with greater specificity; in contrast, the conventional decision analytic measure, net benefit, rank ordered tests consistent with clinical intuition, with the highest sensitivity test favored. While it might be argued that F scores can be made consistent with utility by choosing a value of β different from 1, we found it is impossible to rationally prespecify β for any given clinical scenario, as even small changes in prevalence led to undesirable rank orderings for a given β being inconsistent with utility.</p><p><strong>Conclusion: </strong>The F score ranks diagnostic tests and prediction models inconsistently with their clinical utility. Moreover, the F score does not have an interpretable unit, does not allow for a comparison with a strategy assuming all are negative, and requires dichotomization of models. In contrast, standard decision-analytic measures - net benefit and decision curve analysis - allow rational and consistent choice of weighting, have an interpretable unit, can evaluate the strategy of assuming all are negative, and do not require dichotomization of continuous models. Consistent with TRIPOD AI we recommend that net benefit, alongside discrimination and calibration, be used for the evaluation of diagnostic tests and prediction models.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"30"},"PeriodicalIF":2.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12683848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI in healthcare: to explain, to predict, or to describe?","authors":"Alex Carriero, Anne de Hond, Bram Cappers, Fernando Paulovich, Sanne Abeln, Karel Gm Moons, Maarten van Smeden","doi":"10.1186/s41512-025-00213-8","DOIUrl":"10.1186/s41512-025-00213-8","url":null,"abstract":"<p><p>Explainable Artificial Intelligence (AI) methods are designed to provide information about how AI-based models make predictions. In healthcare, there is a widespread expectation that these methods will provide relevant and accurate information about a model's inner-workings to different stakeholders (ranging from patients and healthcare providers to AI and medical guideline developers). This is a challenging endeavor since what qualifies as relevant information may differ greatly depending on the stakeholder. For many stakeholders, relevant explanations are causal in nature, yet, explainable AI methods are often not able to deliver this information. Using the Describe-Predict-Explain framework, we argue that Explainable AI methods are good descriptive tools, as they may help to describe how a model works but are limited in their ability to explain why a model works in terms of true underlying biological mechanisms and cause-and-effect relations. This limits the suitability of explainable AI methods to provide actionable advice to patients or to judge the face validity of AI-based models.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"29"},"PeriodicalIF":2.6,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12679721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145679578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the versatility of routinely measured prognostic factors.","authors":"Hamish Innes, Philip J Johnson","doi":"10.1186/s41512-025-00206-7","DOIUrl":"10.1186/s41512-025-00206-7","url":null,"abstract":"<p><strong>Background: </strong>Some prognostic factors (PF) are \"versatile\" insofar as they predict diverse health outcomes (age is an exemplar par excellence). In this study, we sought to quantify the versatility of commonly measured PFs.</p><p><strong>Methods: </strong>Participants from the UKB (UK biobank) study were followed from enrolment until the date of outcome or censoring. Over 800 incident adverse outcomes were considered, based on a unique 3-digit ICD code (A00, A01, A02, etc.). Twenty-four routine PFs-including renal, liver function and blood count biomarkers-featured in this analysis. Cox regression was used to determine the association between each PF with time to each outcome event. The number of statistically significant associations, direction of the association (positive/negative) and the median log hazard ratio (LHR) were determined for each PF. Data were visualised using Volcano plots.</p><p><strong>Results: </strong>The analysis included up to 502,408 UKB participants followed for 12.4 years. PFs with the greatest number of statistically significant associations were age (563/836; median LHR: 0.47), waist-hip ratio (530/836; LHR: 0.37) and hand-grip strength (416/836; LHR: 0.27). Conversely, PFs with the lowest number of significant associations were diastolic blood pressure (138/835; LHR: 0.11) and total protein (138/835; LHR: 0.11). Positive correlation was observed between the number of events a PF was associated with and the average effect size for those associations.</p><p><strong>Conclusion: </strong>A wide spectrum exists between the most and least versatile PFs. In addition to age, waist-hip ratio and handgrip strength exhibit high versatility. Understanding PF versatility has implications for optimising the development/performance of prognostic models.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"25"},"PeriodicalIF":2.6,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12676796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Why we do need explainable AI for healthcare.","authors":"Giovanni Cinà, Tabea E Röber, Rob Goedhart, Ş İlker Birbil","doi":"10.1186/s41512-025-00209-4","DOIUrl":"10.1186/s41512-025-00209-4","url":null,"abstract":"<p><p>The recent uptake in certified Artificial Intelligence (AI) tools for healthcare applications has renewed the debate around their adoption. Explainable AI, the sub-discipline promising to render AI devices more transparent and trustworthy, has also come under scrutiny as part of this discussion. Some experts in the medical AI space debate the reliability of Explainable AI techniques, expressing concerns on their use and inclusion in guidelines and standards. Revisiting such criticisms, this article offers a balanced perspective on the utility of Explainable AI, focusing on the specificity of clinical applications of AI and placing them in the context of healthcare interventions. Against its detractors and despite valid concerns, we argue that the Explainable AI research program is still central to human-machine interaction and ultimately a useful tool against loss of control, a danger that cannot be prevented by rigorous clinical validation alone.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"24"},"PeriodicalIF":2.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12670843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145656552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of clinical prediction models for chronic kidney disease among people with diabetes: external validation using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).","authors":"Jason E Black, David Jt Campbell, Paul E Ronksley, Kerry A McBrien, Tyler S Williamson","doi":"10.1186/s41512-025-00208-5","DOIUrl":"10.1186/s41512-025-00208-5","url":null,"abstract":"<p><strong>Background: </strong>Several clinical prediction models that predict the risk of chronic kidney disease (CKD) in people with diabetes have been developed; however, these models lack external validation demonstrating accurate predictions in Canadian primary care. We externally validated existing clinical prediction models for CKD in Canadian primary care data, overall and across subgroups defined by sex/gender, age, comorbidities, and neighbourhood-level deprivation.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) electronic medical record database (2014-2019). We identified models that use demographic, health behaviour, clinical and diabetes-related characteristics to predict incident CKD based on two recent systematic reviews and included models with sufficient predictors in CPCSSN (≤ 1 unavailable) and eGFR-based CKD definitions. We included adult patients (18 +) with diabetes without an existing diagnosis of CKD. We identified incident cases of CKD within 5 years based on ≥ 2 laboratory values corresponding to eGFR < 60 mL/min/1.73 m² separated by ≥ 90 days and ≤ 1 year. For each model, we estimated the discrimination, precision, recall, and calibration within CPCSSN.</p><p><strong>Results: </strong>Among 37,604 patients with diabetes, 14.6% met diagnostic criteria for CKD within 5 years. Overall performance of the 13 included CKD prediction models in CPCCSN was mixed: three models displayed moderate to strong discrimination (areas under the receiver-operating characteristic curves [AUROCs] > 0.70), whereas other AUROCs were as Low as 0.508. After model updating, calibrations were heterogeneous with most models displaying some miscalibration. Some subgroups displayed considerable differences in performance: discriminative performance (AUROC) declined with increasing age and number of comorbidities, whereas the precision and recall improved with increasing age and number of comorbidities. We observed no difference in performance according to sex/gender or deprivation quintile.</p><p><strong>Conclusions: </strong>Three models displayed moderate to strong performance predicting CKD among CPCSSN patients. Next, these models should be evaluated for their impact on practitioner and patient outcomes when implemented in clinical practice. If successful, these models hold promise in achieving widespread adoption to help identify those at highest risk of CKD and guide therapies that may prevent or delay CKD and related sequelae (e.g., end-stage renal disease) among people with diabetes.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"26"},"PeriodicalIF":2.6,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12604207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145490972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Will large language models transform clinical prediction?","authors":"Yusuf Yildiz, Goran Nenadic, Meghna Jani, David A Jenkins","doi":"10.1186/s41512-025-00211-w","DOIUrl":"10.1186/s41512-025-00211-w","url":null,"abstract":"<p><strong>Objective: </strong>Large language models (LLMs) are attracting increasing interest in healthcare. This commentary evaluates the potential of LLMs to improve clinical prediction models (CPMs) for diagnostic and prognostic tasks, with a focus on their ability to process longitudinal electronic health record (EHR) data.</p><p><strong>Findings: </strong>LLMs show promise in handling multimodal and longitudinal EHR data and can support multi-outcome predictions for diverse health conditions. However, methodological, validation, infrastructural, and regulatory challenges remain. These include inadequate methods for time-to-event modelling, poor calibration of predictions, limited external validation, and bias affecting underrepresented groups. High infrastructure costs and the absence of clear regulatory frameworks further prevent adoption.</p><p><strong>Implications: </strong>Further work and interdisciplinary collaboration are needed to support equitable and effective integration into the clinical prediction. Developing temporally aware, fair, and explainable models should be a priority focus for transforming clinical prediction workflow.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"28"},"PeriodicalIF":2.6,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12590740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeted test evaluation: five suggestions for refining the framework for designing diagnostic accuracy studies with clear study hypotheses.","authors":"Werner Vach","doi":"10.1186/s41512-025-00207-6","DOIUrl":"10.1186/s41512-025-00207-6","url":null,"abstract":"<p><p>Five years ago, Korevaar and colleagues proposed a framework for designing diagnostic accuracy studies, focusing on the definition of clear study hypotheses. This proposal filled a gap and was well received by the scientific community. In this commentary, I suggest five potential refinements. They aim at increasing the flexibility of the framework while pertaining its logical consistency. The refinements address the following five topics: (1) The relationship between minimal criteria and the choice of the null hypothesis region; (2) The potential to allow compensation between sensitivity and specificity; (3) The possibility to use other pairs than sensitivity and specificity; (4) The potential phrasing as an estimation problem; (5) The advantages of directly moving to a comparative accuracy study.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"27"},"PeriodicalIF":2.6,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12584363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145440237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibration of cause-specific absolute risk for external validation using each cause-specific hazards model in the presence of competing events.","authors":"Sarwar I Mozumder, Sarah Booth, Richard D Riley, Mark J Rutherford, Paul C Lambert","doi":"10.1186/s41512-025-00197-5","DOIUrl":"10.1186/s41512-025-00197-5","url":null,"abstract":"<p><strong>Background: </strong>When developing/validating prognostic models, it is typical to assess calibration between predicted and observed risks - either in the development dataset or in an external sample. For competing risks data, correct specification of more than one model may be required to ensure well-calibrated predicted risks for the event of interest. Furthermore, interest may be in the predicted risks of the event of interest, competing events and all-causes. Therefore, calibration must be assessed simultaneously using various measures.</p><p><strong>Methods: </strong>We focus on the calibration of prediction models for external validation using a cause-specific hazards approach. We propose that miscalibration for cause-specific hazard models be assessed using components specific to each model through the complement of the cause-specific survival alongside the assessment of the calibration of the cause-specific absolute risks. We simulated a range of scenarios to illustrate how to identify which model(s) are mis-specified in an external validation setting. Calibration plots and calibration statistics (calibration slope, calibration-in-the-large) are presented alongside performance measures such as the Brier score and Index of Prediction Accuracy. We use pseudo-observations to calculate observed risks and generate a smooth calibration curve with restricted cubic splines. We fitted flexible parametric survival models to the simulated data to flexibly estimate baseline cause-specific hazards for the prediction of individual cause-specific absolute risks.</p><p><strong>Results: </strong>Our simulations illustrate that miscalibration due to changes in the baseline cause-specific hazards in external validation data is better identified using components from each cause-specific model. A mis-calibrated model on one cause could lead to poor calibration of the predicted absolute risks for each cause of interest, including the all-cause absolute risk. This is because prediction of a single cause-specific absolute risk is impacted by effects of variables on the cause of interest and competing events.</p><p><strong>Conclusions: </strong>If accurate predictions for both all-cause and each cause-specific absolute risks are of interest, this is best achieved by developing and validating models via the cause-specific hazards approach. For each cause-specific model, researchers should evaluate calibration plots separately using the complement of the cause-specific survival function to reveal the cause of any miscalibration. However, this also requires careful consideration of dependent censoring which must be sufficiently accounted for.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"23"},"PeriodicalIF":2.6,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12519608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Counterfactual prediction from machine learning models: transportability and joint analysis for model development and evaluation using multi-source data.","authors":"Sarah C Voter, Issa J Dahabreh, Christopher B Boyer, Habib Rahbar, Despina Kontos, Jon A Steingrimsson","doi":"10.1186/s41512-025-00201-y","DOIUrl":"10.1186/s41512-025-00201-y","url":null,"abstract":"<p><strong>Background: </strong>When a machine learning model is developed and evaluated in a setting where the treatment assignment process differs from the setting of intended model deployment, failure to account for this difference can lead to suboptimal model development and biased estimates of model performance.</p><p><strong>Methods: </strong>We consider the setting where data from a randomized trial and an observational study emulating the trial are available for machine learning model development and evaluation. We provide two approaches for estimating the model and assessing model performance under a hypothetical treatment strategy in the target population underlying the observational study. The first approach uses counterfactual predictions from the observational study only and relies on the assumption of conditional exchangeability between treated and untreated individuals (no unmeasured confounding). The second approach leverages the exchangeability between treatment groups in the trial (supported by study design) to \"transport\" estimates from the trial to the population underlying the observational study, relying on an additional assumption of conditional exchangeability between the populations underlying the observational study and the randomized trial.</p><p><strong>Results: </strong>We examine the assumptions underlying both approaches for fitting the model and estimating performance in the target population and provide estimators for both objectives. We then develop a joint estimation strategy that combines data from the trial and the observational study, and discuss benchmarking of the trial and observational results.</p><p><strong>Conclusions: </strong>Both the observational and transportability analyses can be used to fit a model and estimate performance under a counterfactual treatment strategy in the population underlying the observational data, but they rely on different assumptions. In either case, the assumptions are untestable, and deciding which method is more appropriate requires careful contextual consideration. If all assumptions hold, then combining the data from the observational study and the randomized trial can be used for more efficient estimation.</p>","PeriodicalId":72800,"journal":{"name":"Diagnostic and prognostic research","volume":"9 1","pages":"22"},"PeriodicalIF":3.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}