Measuring clinical uncertainty and equipoise by applying the agreement study methodology to patient management decisions
Bibliographic record
Abstract
BACKGROUND: Clinical uncertainty and equipoise are vague notions that play important roles in contemporary problems of medical care and research, including the design and conduct of pragmatic trials. Our goal was to show how the reliability study methods normally used to assess diagnostic tests can be applied to particular management decisions to measure the degree of uncertainty and equipoise regarding the use of rival management options. METHODS: We first use thrombectomy in acute stroke as an illustrative example of the method we propose. We then review, item by item, how the various design elements of diagnostic reliability studies can be modified in order to measure clinical uncertainty. RESULTS: The thrombectomy example shows sufficient disagreement and uncertainty to warrant the conduct of additional randomized trials. The general method we propose is that a sufficient number of diverse individual cases sharing a similar clinical problem and covering a wide spectrum of clinical presentations be assembled into a portfolio that is submitted to a variety of clinicians who routinely manage patients with the clinical problem. DISCUSSION: Clinicians are asked to independently choose one of the predefined management options, which are selected from those that would be compared within a randomized trial that would address the clinical dilemma. Intra-rater agreement can be assessed at a later time with a second evaluation. Various professional judgments concerning individual patients can then be compared and analyzed using kappa statistics or similar methods. Interpretation of results can be facilitated by providing examples or by translating the results into clinically meaningful summary sentences. CONCLUSIONS: Measuring the uncertainty regarding management options for clinical problems may reveal substantial disagreement, 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 | 0.681 | 0.823 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".