Evidence‐based clinical policy: case report of a reproducible process to encourage understanding and evaluation of evidence
Bibliographic record
Abstract
We report within a case study a reproducible process to facilitate the explicit incorporation of evidence by a multidisciplinary group into clinical policy development. To support the decision-making of a multidisciplinary Intersectoral Advisory Group (IAG) convened by the Royal Australasian College of Physicians Health Policy Unit, a systematic review of randomized controlled trials about environmental tobacco smoke and smoking cessation interventions in paediatric settings was first undertaken. As reported in detail here, IAG members were then formally engaged in a transparent and replicable process to understand and interpret the synthesized evidence and to proffer their independent reactions regarding policy, practice and research. Our intention was to ensure that all IAG members were democratically engaged and made aware of the available evidence. As clinical policy must engage stakeholder representatives from diverse backgrounds, a process to equalize understanding of the evidence and 'democratize' judgment about its implications is needed. Future research must then examine the benefits of such explicit steps when guidelines, in turn, are implemented. We hypothesize that changes to future practice will be more likely if processes undertaken to develop guidelines are transparent to clinicians and other target groups.
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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 | 0.240 | 0.476 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.020 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 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".