Journal of Clinical Sleep Medicine最新文献

{"title":"Evolving trends in novel sleep tracking and sleep testing technology publications between 2020-2022.","authors":"Steven Holfinger, Sharon Schutte-Rodin, Dulip Ratnasoma, Ambrose A Chiang, Kelly Baron, Maryann Deak, Evin Jerkins, Julie Baughn, Kevin Gipson, Reut Gruber, Jennifer N Miller, Shalini Paruthi, Sachin Shaw, Fariha Abbasi-Feinberg, Eric Olsen, Anuja Bandyopadhyay","doi":"10.5664/jcsm.11562","DOIUrl":"https://doi.org/10.5664/jcsm.11562","url":null,"abstract":"<p><strong>Study objectives: </strong>To update sleep medicine providers regarding (1) published research on the uses and performance of novel sleep tracking and testing technologies (2) the use of artificial intelligence to acquire and process sleep data and (3) research trends and gaps regarding the development and/or evaluation of these technologies.</p><p><strong>Methods: </strong>Medline and Embase electronic databases were searched for studies utilizing screening and diagnostic sleep technologies, published between 2020 and 2022 in journals focusing on human sleep. Studies' quality was determined based on the Study Design criteria of The Oxford Centre for Evidence-Based Medicine Levels of Evidence.</p><p><strong>Results: </strong>96 of 3849 articles were included. Most studies were adult performance evaluation (validation) studies, often comparing a novel technology to polysomnography. Sleep tracker publications tended to be USA-based, non-industry funded, performance studies on healthy adults using non-FDA (Food and Drug Administration) cleared technologies. Sleep apnea testing technologies were more frequently industry-funded and FDA-cleared. All studied technologies utilized software with an algorithm and/or artificial intelligence. Few studies used randomized control designs, or accounted for recruitment/attrition biases associated with participants' age, race/ethnicity, or comorbid health conditions.</p><p><strong>Conclusions: </strong>Evidence-based publications have not kept pace with the proliferation and landscape of' consumer and clinical sleep technologies. Due to the variance in technologies used within sleep research, careful review of the software used within studies is recommended. Future publications may fill identified gaps by including underrepresented populations, maintaining independence from industry, and through rigorous study design.</p>","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical validation of a wireless patch-based polysomnography system.","authors":"Christian Viniol, Wolfgang Galetke, Holger Woehrle, Georg Nilius, Christoph Schöbel, Winfried Randerath, James Leiter, Sebastian Canisius, Hartmut Schneider","doi":"10.5664/jcsm.11524","DOIUrl":"10.5664/jcsm.11524","url":null,"abstract":"<p><strong>Study objectives: </strong>Onera Health has developed the first wireless, patch-based, type-II PSG system, the Onera Sleep Test System (STS), to allow studies to be performed unattended at the patient's home or in any bed at a medical facility. The goal of this multicenter study was to validate data collected from the patch-based PSG to a traditional PSG for sleep staging and AHI.</p><p><strong>Methods: </strong>Simultaneous traditional PSG and patch-based PSG study data were obtained in a sleep laboratory from 206 participants with a suspected sleep disorder recruited from 7 clinical sites. Blinded, randomized scoring of the traditional PSG and patch-based PSG recordings was completed according to <i>The AASM Manual for the Scoring of Sleep and Associated Events</i>, version 2.6 criteria by three independent scorers.</p><p><strong>Results: </strong>Concordance correlation coefficients were high between the patch-based device and traditional PSG across essential sleep and respiratory variables - TST (0.87); Wake (0.84); NREM (0.80); N1 (0.72); N2 (0.71); N3 (0.64); REM (0.80) and AHI (0.94). There was substantial agreement between epoch sleep staging scored on the patch-based device and traditional PSG (average Cohen's kappa of 0.62 ± 0.13 across all scorers).</p><p><strong>Conclusions: </strong>The patch-based type-II PSG had a similar performance on sleep staging and respiratory variables when compared to Traditional PSG, thus making it possible to use the patch-based PSG for a routine PSG study. These results open the possibility of performing unattended PSG studies efficiently and accurately outside the sleep laboratory improving access to high quality sleep assessments for patients with sleep disorders.</p><p><strong>Clinical trial registration: </strong>Registry: ClinicalTrials.gov; Identifier: NCT05310708.</p>","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142958221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of two different wrist actigraphy devices in the adult population.","authors":"Mario Henríquez-Beltrán, Iván D Benítez, Ivan Juez-Garcia, Jordi de Batlle, Thalía Belmonte, Anna Galan Gonzalez, Matías Burgos, Rocio Sanhueza, Claudia Jeria, Daniel Solomons, Adriano D S Targa, Ferran Barbé, Gonzalo Labarca","doi":"10.5664/jcsm.11536","DOIUrl":"https://doi.org/10.5664/jcsm.11536","url":null,"abstract":"<p><strong>Study objectives: </strong>Actigraphy devices are used in sleep medicine. The Actiwatch 2 (Philips Respironics) was an example of a frequently used device in this field. Nevertheless, the discontinuation of this device has led to an increased necessity for the implementation of other available actigraphy methods capable of providing objective information. The objective of this study was to assess the performance of the new ActTrust 2 compared to the Actiwatch 2 model.</p><p><strong>Methods: </strong>This observational prospective study included nine subjects (77.760 activity logs), who were monitored for seven days using two actigraphy wrist devices (ActTrust 2 and Actiwatch 2) simultaneously. The following variables were evaluated: Mesor, amplitude, and acrophase, Intradaily Variability (IV), Interdaily Stability (IS), Relative Amplitude (RA), the mean of five consecutive hours with the lowest activity (L5), and the ten consecutive hours with the highest activity (M10). Furthermore, total sleep time (TST), time in bed (TIB), sleep efficiency (SE), sleep onset latency (SOL), wake after sleep onset (WASO), and awakenings were also included.</p><p><strong>Results: </strong>Actigraphy models indicated statistically significant differences in activity levels. Regarding the analysis of circadian rest-activity rhythms, M10, Mesor, and amplitude also exhibited these differences. Furthermore, the analysis of sleep-wakefulness revealed significant differences in the sleep onset latency (SOL) and the number of awakenings.</p><p><strong>Conclusions: </strong>The ActTrust 2 and Actiwatch 2 models showed equivalent results in measuring circadian rest-activity rhythm and sleep. However, caution is advised when interpreting parameters like Mesor, amplitude, SOL, awakenings, and M10 variables.</p>","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Context-aware analysis enhances autoscoring accuracy of home sleep apnea testing.","authors":"Frederik Massie, Steven Vits, Johan Verbraecken, Jeroen Bergmann","doi":"10.5664/jcsm.11534","DOIUrl":"https://doi.org/10.5664/jcsm.11534","url":null,"abstract":"<p><strong>Study objectives: </strong>Home sleep apnea testing based on peripheral arterial tonometry (P-HSAT) is increasingly being deployed because of its ability to test for multiple nights. However, P-HSATs do not have access to modalities such as airflow and cortical arousals and instead rely on alternative sources of information to detect respiratory events. This results in an a-priori performance disadvantage. In this study, we describe the Panorama algorithm, which aims to reduce this disadvantage by incorporating information from characteristically repetitive sequences in physiological changes associated with respiratory events. These include changes in peripheral arterial tone, pulse rate, and oxygen saturation. The method was designed to facilitate manual review by providing the scoring rationale for each respiratory event.</p><p><strong>Methods: </strong>The method was developed and evaluated using a dataset of 266 participants from a multicentric cohort suspected of having obstructive sleep apnea (OSA). All participants underwent simultaneous polysomnography (PSG) and P-HSAT, and all PSG data were double-scored. Scoring was performed according to the 3% and 4% rules for hypopnea scoring. Clinical endpoint parameters, including the OSA severity categorization accuracy and Cohen's Kappa, were selected to compare the algorithm to a conventional context-unaware algorithm. Data analysis and reporting followed the TRIPOD+AI reporting guidance for prediction models that use machine learning.</p><p><strong>Results: </strong>Regarding OSA severity categorization accuracy, the Panorama algorithm significantly outperformed context-unaware autoscoring by 9% using 3% rule scoring and 7% using 4% rule scoring.</p><p><strong>Conclusions: </strong>The context-aware method significantly improves the performance of P-HSAT while still facilitating scoring review by providing event-specific scoring rationale.</p><p><strong>Clinical trial registration: </strong>Registry: ClinicalTrials.gov; Title: A Validation Study of the NightOwl PAT-based Home Sleep Apnea Test; Identifier: NCT04191668; URL: https://clinicaltrials.gov/ct2/show/NCT04191668.</p>","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142923631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adjusting the apnea-hypopnea index in children with a low percentage of REM sleep and its potential impact on OSA diagnosis and severity.","authors":"Amal Alnaimi, Haneen Toma, Ahmed Abushahin, Antonisamy Belavendra, Mutasim Abu-Hasan","doi":"10.5664/jcsm.11532","DOIUrl":"https://doi.org/10.5664/jcsm.11532","url":null,"abstract":"<p><p><b>Study Objectives:</b> A decrease in REM time during polysomnography (PSG) in patients with obstructive sleep apnea (OSA) can result in underestimation of apnea hypopnea index (AHI). We propose adjusting AHI to normalized REM% in subjects with REM% ≤15% to avoid under diagnosis of OSA. <b>Methods:</b> All children who completed diagnostic PSG from 2016 to 2023 with REM% of ≤ 15% of total TST were selected for adjustment. AHI Adjustment was done by multiplying AHI by a normalization factor (20%)/REM%). Changes in OSA diagnosis and severity were evaluated before vs after adjustment. Intra class comparison and Bland-Altman plots were used to evaluate agreement level of adjusted AHI vs non-adjusted AHI with REM AHI as the reference. P value <0.05 was significant. <b>Results:</b> Of 389 children reviewed, only 79 (20%) children had low REM% of ≤ 15%. Median (range) age was 12.8(0.9-18) years with Male/female ratio 2.3/1. Mean (SD) sleep efficiency was 64.7% (12.3). Mean (SD) REM% was 10.5% (3.4). Median AHI (range) before AHI adjustment was 1.7(0-44) events/hour vs 4.1 (0-74.4) events/hour after AHI adjustment (P<0.001). Adjusted AHI had better agreement with REM- AHI (ICC=0.691; 95% CI: 0.58,0.80) than non-Adjusted AHI (ICC=0.485; 95% CI: 0.39,0.58). AHI adjustment changed diagnosis from normal to mild OSA in 12 (15%) patients, from mild to moderate OSA in 7(9%) patients, and from moderate to severe OSA in 9 (11%) patients (p=0.044). <b>Conclusions:</b> Adjusting AHI in patients with low REM% to a normalized REM% can help avoid underdiagnosis of OSA and/or underestimation of its severity.</p>","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inspiratory positive pressure modulation to minimize respiratory control instability.","authors":"Robert Joseph Thomas","doi":"10.5664/jcsm.11548","DOIUrl":"https://doi.org/10.5664/jcsm.11548","url":null,"abstract":"","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.","authors":"Jui-En Lo, Christopher N Schmickl, Florin Vaida, Shamim Nemati, Karandeep Singh, Scott A Sands, Robert L Owens, Atul Malhotra, Jeremy E Orr","doi":"10.5664/jcsm.11498","DOIUrl":"10.5664/jcsm.11498","url":null,"abstract":"<p><p><b>Study Objectives:</b> Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea (OSA); however some people have residual respiratory events or require significantly higher CPAP pressure while on therapy. Our objective was to develop predictive models for CPAP outcomes and assess whether the inclusion of physiological traits enhances prediction. <b>Methods:</b> We constructed predictive models from baseline information for subsequent residual apnea-hypopnea index (AHI) and optimal CPAP pressure. We compared models utilizing clinical variables with those incorporating both clinical and physiological factors. Furthermore, we assessed the performance of regression versus machine learning. All performances, including root mean square error (RSME), R-squared, accuracy, and area under the curve (AUC), were evaluated using a five-fold cross validation with ten repeats. <b>Results:</b> For predicting residual AHI, random forest models outperformed regression models, and models that incorporated both clinical and physiological variables also outperformed models using only clinical variables across all performance metrics. Random forest using both clinical features and physiological traits achieved the best performance. In both regression and random forest models, central apnea index is found to be the most important feature in predicting residual AHI. For predicting CPAP pressure, there was no additional predictive value of physiological traits or random forest modeling. <b>Conclusions:</b> Our findings demonstrated that the combined use of clinical and physiological variables yields the most robust predictive models for residual AHI, with random forest models performing best. These findings support the notion that prediction of OSA therapy outcomes may be improved by more flexible models using machine learning, potentially in combination with physiology-based models.</p>","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enough is enough: strict hypopnea criteria exacerbate sleep-related health disparities in women.","authors":"Lindsay M McCullough, Hrayr Attarian","doi":"10.5664/jcsm.11550","DOIUrl":"https://doi.org/10.5664/jcsm.11550","url":null,"abstract":"","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142916042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Treatment of restless legs syndrome and periodic limb movement disorder: an American Academy of Sleep Medicine clinical practice guideline.","authors":"John W Winkelman, J Andrew Berkowski, Lourdes M DelRosso, Brian B Koo, Matthew T Scharf, Denise Sharon, Rochelle S Zak, Uzma Kazmi, Yngve Falck-Ytter, Anita V Shelgikar, Lynn Marie Trotti, Arthur S Walters","doi":"10.5664/jcsm.11390","DOIUrl":"10.5664/jcsm.11390","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;This guideline establishes clinical practice recommendations for treatment of restless legs syndrome (RLS) and periodic limb movement disorder (PLMD) in adults and pediatric patients.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The American Academy of Sleep Medicine (AASM) commissioned a task force of experts in sleep medicine to develop recommendations and assign strengths based on a systematic review of the literature and an assessment of the evidence using the grading of recommendations assessment, development, and evaluation methodology. The task force provided a summary of the relevant literature and the certainty of evidence, the balance of benefits and harms, patient values and preferences, and resource use considerations that support the recommendations. The AASM Board of Directors approved the final recommendations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Good practice statement: &lt;/strong&gt;The following good practice statement is based on expert consensus, and its implementation is necessary for the appropriate and effective management of patients with RLS.&lt;/p&gt;&lt;p&gt;&lt;p&gt;1. In all patients with clinically significant RLS, clinicians should regularly test serum iron studies including ferritin and transferrin saturation (calculated from iron and total iron binding capacity). Testing should ideally be administered in the morning avoiding all iron-containing supplements and foods at least 24 hours prior to blood draw. Analysis of iron studies greatly influences the decision to use oral or intravenous (IV) iron treatment. Consensus guidelines, which have not been empirically tested, suggest that supplementation of iron in adults with RLS should be instituted with oral or IV iron if serum ferritin ≤ 75 ng/mL or transferrin saturation &lt; 20%, and only with IV iron if serum ferritin is between 75 and 100 ng/mL. In children, supplementation of iron should be instituted for serum ferritin &lt; 50 ng/mL with oral or IV formulations. These iron supplementation guidelines are different than for the general population.&lt;/p&gt;&lt;p&gt;&lt;p&gt;2. The first step in the management of RLS should be addressing exacerbating factors, such as alcohol, caffeine, antihistaminergic, serotonergic, antidopaminergic medications, and untreated obstructive sleep apnea.&lt;/p&gt;&lt;p&gt;&lt;p&gt;3. RLS is common in pregnancy; prescribers should consider the pregnancy-specific safety profile of each treatment being considered.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Recommendations: &lt;/strong&gt;The following recommendations are intended as a guide for clinicians in choosing a specific treatment for RLS and PLMD in adults and children. Each recommendation statement is assigned a strength (\"strong\" or \"conditional\"). A \"strong\" recommendation (ie, \"We recommend…\") is one that clinicians should follow under most circumstances. The recommendations listed below are ranked in the order of strength of recommendations and grouped by class of treatments within each PICO (Patient, Intervention, Comparator, Outcome) question. Some recommendations includ","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":"137-152"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11701286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sleep disturbances associated with <i>DEAF1</i> pathogenic variants.","authors":"Pedro Guerreiro, Mariana Moysés-Oliveira, Mayara Paschalidis, Anna Kloster, Lais Cunha, Tais Bassani Deconto, Amanda Cristina Mosini, Bruna Pereira Marquezini, Luana Nayara Gallego Adami, Monica L Andersen, Sergio Tufik","doi":"10.5664/jcsm.11316","DOIUrl":"10.5664/jcsm.11316","url":null,"abstract":"<p><p>Neurodevelopmental disorders and sleep disturbances share genetic risk factors. <i>DEAF1</i> genetic variants are associated with rare syndromes in which sleep disturbances are commonly reported, yet the specific sleep disorders in these patients, and the molecular mechanisms underlying this association, are unknown. We aimed to pinpoint specific biological processes that may be disrupted by pathogenic variants in this gene, comparing a list of DEAF1 regulatory target genes with a list of insomnia-associated genes, and using the intersect gene list as the input for pathway enrichment analysis. Thirty-nine DEAF1 regulatory targets were also identified as insomnia-associated genes, and the intersecting gene list was found to be strongly associated with immune processes, ubiquitin-mediated proteolysis pathways, and regulation of the cell cycle. This preliminary study highlights pathways that may be disrupted by <i>DEAF1</i> pathogenic mutations and might be putative factors underlying the manifestation of insomnia in patients harboring such variants.</p><p><strong>Citation: </strong>Guerreiro P, Moysés-Oliveira M, Paschalidis M, et al. Sleep disturbances associated with <i>DEAF1</i> pathogenic variants. <i>J Clin Sleep Med</i>. 2025;21(1):207-210.</p>","PeriodicalId":50233,"journal":{"name":"Journal of Clinical Sleep Medicine","volume":" ","pages":"207-210"},"PeriodicalIF":3.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}