How do we know if a clinical practice guideline is good? A response to Djulbegovic and colleagues' use of fast‐and‐frugal decision trees to improve clinical care strategies
Bibliographic record
Abstract
Clinical practice guidelines (CPGs) and clinical pathways have become important tools for improving the uptake of evidence-based care. Where CPGs are good, adherence to the recommendations within is thought to result in improved patient outcomes. However, the usefulness of such tools for improving patient important outcomes depends both on adherence to the guideline and whether or not the CPG in question is good. This begs the question of what it is that makes a CPG good? In this issue of the Journal, Djulbegovic and colleagues offer a theory to help guide the development of CPGs. The "fast-and-frugal tree" (FFT) heuristic theory is purported to provide the theoretical structure needed to quantitatively assess clinical guidelines in practice, something that the lack of theory to guide CPG development has precluded. In this paper, I examine the role of FFTs in providing an adequate theoretical framework for developing CPGs. In my view, positioning guideline development within the FFT framework may help with problems related to adherence. However, I believe that FTTs fall short in providing panel members with the theoretical basis needed to justify which factors should be considered when developing a CPG, how information on those factors derived from research studies should be interpreted, and how those factors should be integrated into the recommendation.
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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 | 0.105 | 0.376 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.055 | 0.119 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".