Medical Decision Making最新文献

{"title":"Did You Know That Confounding Could Bias Results Obtained within an Economic Evaluation?","authors":"Simon LaRue, Mike Paulden, Denis Talbot, Jason Robert Guertin","doi":"10.1177/0272989X261474826","DOIUrl":"https://doi.org/10.1177/0272989X261474826","url":null,"abstract":"<p><strong>Background: </strong>The increasing use of real-world data in economic evaluation raises concerns about the potential for confounding bias. Methods to control for this bias have been developed, but there is still much to be understood about how confounding influences economic evaluations.</p><p><strong>Objectives: </strong>To illustrate the impact of unadjusted confounding variables in economic evaluations of observational studies.</p><p><strong>Methods: </strong>We simulated the costs and effectiveness of 2 treatments across 9 possible confounding effect scenarios. We considered these scenarios in the context in which one treatment is more costly and more effective with mild correlation between confounders and outcomes. All scenarios examined the incremental cost and effectiveness of 400 randomly generated individuals, reflecting sample sizes commonly seen within observational economic evaluations. Results were illustrated with the use of cost-effectiveness planes and cost-effectiveness acceptability curves (CEAC).</p><p><strong>Results: </strong>Our simulations illustrate that confounding bias can have a significant effect on incremental costs and incremental effectiveness estimates. These simulations also illustrate that confounders can affect the evaluation of uncertainty by causing a shift in the CEACs. Such results hint that inadequate consideration of confounding bias can potentially lead to flawed judgments about the cost-effectiveness of a treatment.</p><p><strong>Discussion: </strong>Results of economic evaluations can be influenced by confounding when they are conducted in an observational setting. Efforts must be made to limit their impact to ensure an accurate assessment of the economic value of treatments and to prevent potential losses of population health.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261474826"},"PeriodicalIF":2.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892192","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":"A Framework for the Estimation of Quality-Adjusted Life-Years Using Joint Models of Longitudinal and Survival Data.","authors":"Michael J Crowther, Alessandro Gasparini, Sara Ekberg, Federico Felizzi, Elaine Gallagher, Noman Paracha","doi":"10.1177/0272989X261477224","DOIUrl":"https://doi.org/10.1177/0272989X261477224","url":null,"abstract":"<p><strong>Background: </strong>Length and quality of life are frequently combined in health technology assessments to derive a single, generic measure of health improvement due to a treatment or medicine. One such measure is given by quality-adjusted life-years (QALYs), typically calculated assuming discrete health states and quality-of-life values. The accurate estimation of QALYs is crucial for informed decision making in health care policy and resource allocation; however, traditional methods often rely on assumptions that are sometimes biologically and statistically inappropriate. This study aims to develop a framework for estimating QALYs in populations where survival data are collected that addresses these limitations.</p><p><strong>Methods: </strong>A framework for estimating QALYs in continuous time is introduced, based on joint longitudinal-survival models fitted using maximum likelihood; the proposed framework requires patient-level data, including longitudinal health utility values and overall survival. In contrast to the conventional approach, which involves dichotomising health states and separate models, this method allows the estimation of QALYs from a single model while accounting for all the statistical and biological intricacies of the data, providing a more appropriate estimate for cost-effectiveness modelling. The joint modelling approach is validated using Monte Carlo simulation under realistic data-generating mechanisms.</p><p><strong>Results: </strong>Simulations showed that the joint longitudinal-survival modelling approach could recover the true QALYs without bias. Conversely, a comparison method based on calculating QALYs directly from the health utility trajectories, ignoring the survival process, was biased under most scenarios.</p><p><strong>Conclusions: </strong>A new methodology for estimating QALYs within a unified, flexible and extensible framework has been developed. This approach can improve QALY estimation and their use in cost-effectiveness analyses in practice, enabling more timely and robust health technology assessments. User-friendly Stata software is provided.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261477224"},"PeriodicalIF":2.0,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148867589","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":"Best-Worst Scaling as a Values Clarification Method within a Decision Aid for Kidney Treatment Decision Making.","authors":"Semra Ozdemir, Juan Marcos Gonzalez, Eric Andrew Finkelstein","doi":"10.1177/0272989X261479293","DOIUrl":"https://doi.org/10.1177/0272989X261479293","url":null,"abstract":"<p><strong>Background: </strong>Best-worst scaling (BWS) has been increasingly used as a values clarification method to help patients make value-concordant decisions. However, evidence on its use remains limited.</p><p><strong>Objective: </strong>To examine 1) the association between BWS-derived treatment preference scores and patients' stated and real-world treatment choices and 2) whether BWS-identified best-match treatments were concordant with treatment choices among older adults with end-stage kidney disease (ESKD).</p><p><strong>Methods: </strong>We conducted a prospective study among patients aged ≥70 y with incident ESKD in Singapore at the time of decision making. During a renal counseling session with a decision aid, participants completed 2 BWS exercises to clarify values related to treatment choices (dialysis vs kidney supportive care [KSC] and hemodialysis vs peritoneal dialysis). BWS responses were scored by assigning preference weights to treatment-related attributes and aggregating them into continuous treatment preference scores for each treatment option. Logistic regression assessed associations between dialysis preference scores and postcounseling preference for dialysis and dialysis initiation at 6 mo. Concordance was assessed by comparing the BWS-derived best-match treatment, postcounseling preferred treatment, and real-world treatment choice at 6 mo using the Stuart-Maxwell test.</p><p><strong>Results: </strong>Twenty-three patients were enrolled (mean age 77.9 ± 4.7 y; 65% male). Higher BWS scores favoring dialysis were associated with greater odds of preferring dialysis after counseling (odds ratio = 1.31; <i>P</i> = 0.018) and initiating dialysis at 6 mo (odds ratio = 1.41; <i>P</i> = 0.012). For the dialysis and vs decision, the Stuart-Maxwell test indicated no significant differences; 70% selected (<i>P</i> = 0.70) and 65% initiated (<i>P</i> = 0.26) a treatment concordant with the BWS-derived best-match option.</p><p><strong>Conclusions: </strong>This study provides preliminary evidence that BWS-based values clarification is associated with treatment preferences and subsequent real-world treatment initiation among older adults with ESKD.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261479293"},"PeriodicalIF":2.0,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148857825","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 Survival Curves to Incorporate External Evidence: A Simplified Approach without Patient-Level Data.","authors":"David McConnell, Arthur White, Felicity Lamrock, Joan O'Callaghan, Laura McCullagh, Joy Leahy, Lea Trela-Larsen","doi":"10.1177/0272989X261472806","DOIUrl":"https://doi.org/10.1177/0272989X261472806","url":null,"abstract":"<p><strong>Introduction: </strong>Cost-effectiveness modelling often requires the extrapolation of survival data from clinical trials. The choice of extrapolation method is often uncertain and can have a profound effect on the results. We propose an approach that incorporates external evidence (eg, from published studies, registries, or elicited clinical opinion) into the extrapolation process to reduce uncertainty. This method can be applied to adjust previously fitted parametric curves as a secondary analysis, without access to the underlying patient-level data. Adjusted curves can be easily exported and used in spreadsheet-based models.</p><p><strong>Methods: </strong>Standard parametric survival curves are fitted to time-to-event data using maximum likelihood estimation (MLE). These are combined with external evidence on expected cohort-level survival at a future time point(s), formulated as a probability distribution, to generate adjusted survival curves that simultaneously incorporate both observed data and external evidence. Parameter estimation uses importance sampling and multivariate normal approximations of the likelihood. We apply our method to a case study of survival extrapolation from immuno-oncology.</p><p><strong>Results: </strong>Our method resulted in extrapolated survival predictions that were more closely aligned with the external evidence compared with the standard (MLE-based) approach. The incorporation of external evidence decreased the between-distribution variance (reduced structural uncertainty) and for most distributions also decreased within-distribution variance (reduced parametric uncertainty).</p><p><strong>Conclusion: </strong>Our extrapolation method can reduce uncertainty when valid external evidence is available. Only MLE-based parameter estimates are required to implement our method; thus, secondary model users such as health technology assessment bodies can adjust survival extrapolations from existing cost-effectiveness models without access to patient-level data. Implementation is straightforward and computationally efficient, and outputs are easily incorporated into existing cost-effectiveness models.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261472806"},"PeriodicalIF":2.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814763","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":"Is Being Worse-than-Dead Good Enough? On the Added Value of Negative Time-Tradeoff Utilities.","authors":"Michał Jakubczyk","doi":"10.1177/0272989X261479241","DOIUrl":"https://doi.org/10.1177/0272989X261479241","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261479241"},"PeriodicalIF":2.0,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148814808","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":"Determinants of Preferences for Family Involvement in Medical Decision Making: A Vignette-Based Study on a Subsample of The Social Study in Belgium.","authors":"Amina Yakhlaf, Sarah Van de Velde, Edwin Wouters, Veerle Buffel","doi":"10.1177/0272989X261474822","DOIUrl":"https://doi.org/10.1177/0272989X261474822","url":null,"abstract":"<p><strong>Background: </strong>Family involvement (FI) in medical decision making (MDM) is increasingly recognized as a dimension of patient-centered care; however, patient preferences for FI vary across clinical and cultural contexts. In health care systems emphasizing autonomy and direct communication, less is known about how patient characteristics and illness scenarios (cancer vs depression; and mild vs severe depression) influence FI preferences.</p><p><strong>Methods: </strong>We analyzed data from a subsample of a national online probability panel (<i>N</i> = 1,175) collected in 2024. In a survey experiment, respondents evaluated vignettes describing skin cancer and depression; the depression vignette was randomly assigned as mild or severe. Ordered logistic regression models assessed associations between FI preferences and sociodemographic and economic factors as well as religious affiliation.</p><p><strong>Results: </strong>Preference for family-led decision making was uncommon across vignettes (skin cancer: 1.2%; severe depression: 2.7%; mild depression: 1.4%), while the majority preferred patient-led decision making (77.4%, 70.9%, and 81.0%, respectively). Making medical decisions together with family members was most often preferred in cases of severe depression (26.4%), followed by cancer (21.4%) and mild depression (17.6%). Living with a partner and having a migration background (European or non-European) were associated with a greater preference for FI. Respondents identifying as Christian (vs nonreligious/liberal) showed stronger preferences for FI in the cancer and severe depression vignette. Higher educational attainment was associated with weaker preferences for FI, particularly in the cancer vignette.</p><p><strong>Conclusions: </strong>Preferences for family-led decision making were low, with most respondents favoring autonomous decision making. FI preferences varied modestly by illness context and sociodemographic characteristics, particularly migration background, living situation, education, and religious affiliation. These findings underscore the importance of flexible MDM approaches that accommodate heterogeneity in patient preferences.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261474822"},"PeriodicalIF":2.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800739","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":"When Screening Becomes the Default: How Population-Based Programs Influence Cancer Screening Intentions.","authors":"Rebecca Blase, Simone Dohle","doi":"10.1177/0272989X261473635","DOIUrl":"https://doi.org/10.1177/0272989X261473635","url":null,"abstract":"<p><strong>Background: </strong>Population-based screening programs are widely promoted as a cornerstone of preventive health care, yet their structural features may subtly influence individual decision making. In the present research, we investigated whether informed decision making may be challenged by population-based screening programs if these are perceived as establishing a default of participation. We hypothesized that screening intentions are higher if the screening is part of a population-based program and that this effect is mediated by perceived default.</p><p><strong>Methods: </strong>In a preregistered online experiment, 997 German participants aged 18 to 75 years (<i>M</i> = 47.93 y, <i>SD</i> = 15.62 y) were randomly assigned to one of three conditions. Participants read a scenario describing a hypothetical cancer screening offered either (a) as part of a population-based screening program, (b) upon request, or (c) within a research project. The main dependent variable was intention to screen.</p><p><strong>Results: </strong>Screening intentions were significantly higher when the screening was included in a population-based program (<i>M</i> = 4.83) compared with a screening that was available upon request (<i>M</i> = 4.42, <i>P</i> = 0.013, <i>d</i> = 0.22, 95% confidence interval [CI] [0.07, 0.37]) or part of a research project (<i>M</i> = 4.44, <i>P</i> = 0.021, <i>d</i> = 0.21, 95% CI [0.06, 0.36]). A mediation analysis revealed that the status of screening indirectly influenced the intention to participate through its effect on the perceived default of participation.</p><p><strong>Conclusions: </strong>These findings indicate that program structures alone may shape decisions in ways that challenge the aim of informed choice, particularly for screenings with contested benefit-harm profiles. To ensure that population-based programs support rather than undermine informed decision making, communication and program design must promote transparency and enable active, preference-sensitive choices.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261473635"},"PeriodicalIF":2.0,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148800800","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":"Bridging the Gap between Intent and Implementation: A Mixed-Methods Systematic Review of Barriers and Enablers of Shared Decision Making in Postmastectomy Breast Reconstruction.","authors":"Marta Maes-Carballo, Laura Sampol-Ramírez, Natalia de-la-Puente-Mota, Carmen Martínez-Martínez, Yolanda Gómez-Fandiño","doi":"10.1177/0272989X261473627","DOIUrl":"https://doi.org/10.1177/0272989X261473627","url":null,"abstract":"<p><strong>Background: </strong>Postmastectomy breast reconstruction (PMBR) is a preference-sensitive decision that requires alignment between clinical evidence and patient values. Shared decision making (SDM) supports patient-centered care, yet its systematic implementation in PMBR remains inconsistent. This mixed-methods systematic review synthesizes barriers and facilitators to SDM adoption and identifies strategies to improve decision quality, patient experience, and equity.</p><p><strong>Methods: </strong>A systematic review was preregistered (OSF: https://osf.io/mu9hv) and conducted in accordance with PRISMA 2020 guidelines. Six databases (PubMed, Embase, Scopus, Web of Science, Cochrane Library, Trip Database) were searched from inception to November 2025. Eligible studies included qualitative, quantitative, or mixed-methods research examining barriers and/or facilitators to SDM implementation in PMBR across micro (individual), meso (organizational), and macro (system) levels. Data were synthesized using a convergent integrated approach and mapped to the Consolidated Framework for Implementation Research.</p><p><strong>Results: </strong>Thirty-one studies (<i>n</i> = 15-485 participants) from North America, Europe, and Asia were included. SDM interventions-particularly decision aids, digital tools, preconsultation education, and culturally tailored strategies-improved patient knowledge (+6%-32%), decisional clarity, and satisfaction. Decisional conflict decreased by 13 to 25 points, and consultation time was reduced by up to 41%. Facilitators were identified across micro (eg, clinician engagement), meso (eg, multidisciplinary collaboration and organizational support), and macro (eg, supportive implementation strategies), whereas barriers included limited SDM literacy, workflow limitations and insufficient documentation, and structural inequities, respectively. Despite demonstrated effectiveness, sustained implementation was limited.</p><p><strong>Conclusions: </strong>SDM enhances informed and value-concordant decisions in PMBR. Effective implementation requires multilevel strategies, including clinician training, workflow integration, culturally adapted interventions, and organizational support. Future research should prioritize scalable and equitable models to ensure consistent integration of SDM into routine practice.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261473627"},"PeriodicalIF":2.0,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761403","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 Undertesting Bias in Clinical Decision Making: The Role of Test Dichotomization.","authors":"Murat C Mungan","doi":"10.1177/0272989X261471178","DOIUrl":"https://doi.org/10.1177/0272989X261471178","url":null,"abstract":"<p><p>Threshold models, formalized by Pauker and Kassirer, guide clinical decisions about testing and treatment by partitioning pretest probabilities into three action zones using two threshold pretest probabilities: the test threshold and the test-treatment threshold. This derivation implicitly assumes that diagnostic tests possess fixed sensitivity and specificity, effectively treating them as binary predictors. However, many diagnostic tests yield continuous or ordinal outputs, in which case the optimal sensitivity-specificity pair varies with the pretest probability. I demonstrate that abstracting from this dependency leads to an undertesting bias: the testing threshold is inflated and/or the test-treatment threshold is deflated, resulting in a narrower than optimal testing window. This bias systematically undervalues continuous diagnostic tests and leads to their underuse. Clinical decision-making models and guidelines should therefore recognize that optimal test score cutoffs depend on pretest probabilities to avoid this systematic underuse of diagnostic testing.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261471178"},"PeriodicalIF":2.0,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148761483","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":"Development and Validation of a Patient-Centered Scale to Evaluate Decision Aid Usability and Information Overload.","authors":"Nicholas A Pickersgill, Amy L Tin, Sunny Nalavenkata, Mia D Austria, Kristina Stevanovic, Oskar Bergengren, Jason P Gonsky, Jada G Hamilton, Jennifer L Hay, Andrew J Vickers, Angela Fagerlin, Sigrid V Carlsson","doi":"10.1177/0272989X261473303","DOIUrl":"10.1177/0272989X261473303","url":null,"abstract":"<p><strong>Introduction: </strong>Decision aids aim to improve the quality of and satisfaction with decision making. Few scales exist that directly evaluate decision aids from the patient's perspective, particularly with respect to information overload.</p><p><strong>Methods: </strong>We developed a Decision Aid Evaluation Scale with domains assessing acceptability, satisfaction, cognitive load, and helpfulness for decision making. Cognitive interviews and iterative testing were performed. Convergent and discriminant construct validity were assessed by comparing the novel scale with domains from the validated Decisional Conflict Scale (DCS; values clarity, uncertainty, feeling informed subscales). Internal consistency was measured using Cronbach's alpha. The final scale consisted of Likert-type scale items assessing information amount, values clarity, ease of use, cognitive effort required, nervousness, and overall helpfulness. Participants (any sex or gender, aged 40-60 y) completed the scale after viewing a cancer screening decision aid within a hypothetical screening decision scenario.</p><p><strong>Results: </strong>We surveyed 1,249 participants; 778 completed the Decision Aid Evaluation Scale. As hypothesized, a larger proportion of participants who reported low cognitive load had higher DCS values clarity subscale scores than those with high cognitive load (<i>P</i> = 0.003). Participants who found the decision aid helpful had significantly higher DCS values clarity (<i>P</i> < 0.001), informed (<i>P</i> = 0.004), and certainty subscale scores (<i>P</i> < 0.001). Scores on the acceptability questions were higher among those who found the decision aid helpful (<i>P</i> < 0.001), except for questions related to \"length,\" \"relatability of photo,\" and \"photo helped make decision.\" Participants reporting nervousness had lower DCS certainty subscale scores (<i>P</i> < 0.001). Cronbach's alpha for the acceptability and satisfaction domains were 0.74 and 0.75, respectively, indicating internal consistency.</p><p><strong>Conclusions: </strong>We developed a patient-centered scale that captures users' perceptions of decision aid usability and information overload. The scale demonstrated acceptable construct validity and internal consistency, supporting its use in evaluating decision aids from the patient perspective.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":" ","pages":"272989X261473303"},"PeriodicalIF":2.0,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13479721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765424","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}