When is good, good enough? Methodological pragmatism for sustainable guideline development
Bibliographic record
Abstract
BACKGROUND: Continuous escalation in methodological and procedural rigor for evidence-based processes in guideline development is associated with increasing costs and production delays that threaten sustainability. While health research methodologists are appropriately responsible for promoting increasing rigor in guideline development, guideline sponsors are responsible for funding such processes. DISCUSSION: This paper acknowledges that other stakeholders in addition to methodologists should be more involved in negotiating trade-offs between methodological procedures and efficiency in guideline production to produce guidelines that are 'good enough' to be trustworthy and affordable under specific circumstances. The argument for reasonable methodological compromise to meet practical circumstances is consistent with current implicit methodological practice. This paper proposes a conceptual tool as a framework to be used by different stakeholders in negotiating, and explicitly reporting, reasonable compromises for trustworthy as well as cost-worthy guidelines. The framework helps fill a transparency gap in how methodological choices in guideline development are made. The principle, 'when good is good enough' can serve as a basis for this approach. The conceptual tool 'Efficiency-Validity Methodological Continuum' acknowledges trade-offs between validity and efficiency in evidence-based guideline development and allows for negotiation, guided by methodologists, of reasonable methodological compromises among stakeholders. Collaboration among guideline stakeholders in the development process is necessary if evidence-based guideline development is to be sustainable.
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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 | 0.713 | 0.754 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.011 | 0.066 |
| Scholarly communication | 0.033 | 0.043 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.024 | 0.046 |
| Insufficient payload (model declined to judge) | 0.003 | 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".