Getting trustworthy guidelines into the hands of decision-makers and supporting their consideration of contextual factors for implementation globally: recommendation mapping of COVID-19 guidelines
Bibliographic record
Abstract
Published research on COVID-19 is increasing rapidly and integrated in guidelines. The trustworthiness of guidelines can vary depending on the methods used to assemble and evaluate the evidence, the completeness and transparency of reporting on the process undertaken and how conflicts of interest are addressed. With a global consortium of partners and collaborators, we have created a catalogue of COVID-19 recommendations as our direct response to the increased need for structured access to high quality guidance in the field. The COVID19 map of recommendations and gateway to contextualization (https://covid19.recmap.org) is a living project: emerging guideline literature is added on an ongoing basis, allowing granular access to individual recommendations. Building on prior work on mapping recommendations for the World Health Organization tuberculosis guidelines, a novel feature of this map is the self-directed contextualization of the recommendations using the GRADE-Adolopment approach to adopt, adapt or synthesize de novo recommendations for context specific questions. Through our map, stakeholders access the evidence underpinning a recommendation, select what needs to be contextualized and go through the steps of development of adapted recommendations. This one-stop shop portal of evidence-informed recommendations, built with intuitive functionalities, easy to navigate and with a support team ready to guide users across the maps, represents a long-needed tool for decision-makers, guideline developers and the public at large.
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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 | 0.116 | 0.342 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".