Development and use of health outcome descriptors: a guideline development case study
Bibliographic record
Abstract
BACKGROUND: During healthcare guideline development, panel members often have implicit, different definitions of health outcomes that can lead to misunderstandings about how important these outcomes are and how to balance benefits and harms. McMaster GRADE Centre researchers developed 'health outcome descriptors' for standardizing descriptions of health outcomes and overcoming these problems to support the European Commission Initiative on Breast Cancer (ECIBC) Guideline Development Group (GDG). We aimed to determine which aspects of the development, content, and use of health outcome descriptors were valuable to guideline developers. METHODS: We developed 24 health outcome descriptors related to breast cancer screening and diagnosis for the European Commission Breast Guideline Development Group (GDG). Eighteen GDG members provided feedback in written format or in interviews. We then evaluated the process and conducted two health utility rating surveys. RESULTS: Feedback from GDG members revealed that health outcome descriptors are probably useful for developing recommendations and improving transparency of guideline methods. Time commitment, methodology training, and need for multidisciplinary expertise throughout development were considered important determinants of the process. Comparison of the two health utility surveys showed a decrease in standard deviation in the second survey across 21 (88%) of the outcomes. CONCLUSIONS: Health outcome descriptors are feasible and should be developed prior to the outcome prioritization step in the guideline development process. Guideline developers should involve a subgroup of multidisciplinary experts in all stages of development and ensure all guideline panel members are trained in guideline methodology that includes understanding the importance of defining and understanding the outcomes of interest.
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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 | 0.100 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".