Recommendation mapping of the World Health Organization's guidelines on tuberculosis: A new approach to digitizing and presenting recommendations
Bibliographic record
Abstract
OBJECTIVE: Having up-to-date health policy recommendations accessible in one location is in high demand by guideline users. We developed an easy to navigate interactive approach to organize recommendations and applied it to tuberculosis (TB) guidelines of the World Health Organization (WHO). STUDY DESIGN: We used a mixed-methods study design to develop a framework for recommendation mapping with seven key methodological considerations. We define a recommendation map as an online repository of recommendations from several guidelines on a condition, providing links to the underlying evidence and expert judgments that inform them, allowing users to filter and cross-tabulate the search results. We engaged guideline developers, users, and health software engineers in an iterative process to elaborate the WHO eTB recommendation map. RESULTS: Applying the seven-step framework, we included 228 recommendations, linked to 103 guideline questions and organized the recommendation map according to key components of the health question, including the original recommendations and rationale (https://who.tuberculosis.recmap.org/). CONCLUSION: The recommendation mapping framework provides the entire continuum of evidence mapping by framing recommendations within a guideline questions' population, interventions, and comparators domains. Recommendation maps should allow guideline developers to organize their work meaningfully, standardize the automated publication of guidelines through links to the GRADEpro guideline development tool, and increase their accessibility and usability.
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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 | 0.129 | 0.388 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".