Measuring Clinical Uncertainty and Equipoise by Applying the Agreement Study Methodology to Patient Management Decisions
Bibliographic record
Abstract
Abstract Background: Clinical dilemmas in the treatment of patients translate into disagreements in decision-making. Such disagreements can reveal clinical uncertainty that should be addressed through care research. Our goal was to explore the use of reliability study methods to measure the degree of clinical uncertainty and equipoise regarding the use of rival management options prior to the conduct of randomized trials. Methods: The study design resembles an inter-/intra-observer diagnostic reliability study. A portfolio of a sufficient number of diverse individual patients sharing a similar clinical problem and covering a wide spectrum of clinical presentations can be independently submitted to a variety of clinicians who manage that problem. Clinicians are asked to choose one of the predefined management options that are involved in the clinical dilemma. Intra-rater agreement can be assessed at a later time with a second evaluation. Results: Descriptive statistics are presented, and results analyzed using kappa statistics. Interpretation of results can be facilitated by providing examples or by translating the results into clinically meaningful summary sentences. Reporting should follow standard guidelines. Conclusion: Measuring the uncertainty regarding management options for clinical problems may reveal disagreements, provide an empirical foundation for the notion of equipoise, and inform or facilitate the design/conduct of clinical trials to address the clinical dilemma.
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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 | 0.518 | 0.761 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".