Covering patient’s perspective in case‐based critical review articles to improve shared decision making in complex cases
Bibliographic record
Abstract
BACKGROUND: The patient has always been at the centre of the evidence-based medicine model. Case-based critical reviews, such as best-evidence topics, however, are incomplete reflections of the evidence-based medicine philosophy, because they fail to consider the patient's perspective. We propose a new framework, called the 'Shared Decision Evidence Summary' (ShaDES), where the patient's perspective on available treatment options is explicitly included. METHODS: Our framework is grounded in the critical appraisal of a clinical scenario, and the development of a clinical question, including patient characteristics, compared options and outcomes to be improved. Answers to the clinical question are informed by the literature, the evaluation of its quality and its potential usefulness to the clinical scenario. Finally, the evidence synthesis is presented to the patient to facilitate the formulation of an evidence-informed decision about the treatment options. KEY RESULTS: Using three similar but contrasted clinical scenarios of patients with low back pain, we illustrate how considering the patient's preferences on the proposed treatment options impact the bottom line, a synthetic formulation of the answer to the focused question. ShaDES includes clinical and psychosocial components, transformed in a searchable question, with a full search strategy. CONCLUSIONS: ShaDES is a practical framework that may facilitate clinical decisions adapted to psychological, social and other relevant non-clinical characteristics of patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.690 | 0.811 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.034 | 0.018 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.022 | 0.039 |
| Open science | 0.010 | 0.028 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".