How trustworthy guidelines can impact outcomes
Bibliographic record
Abstract
PURPOSE OF REVIEW: If developed using rigorous methods and produced in a timely manner, clinical practice guidelines have the potential to improve patient outcomes. Although the COVID-19 pandemic has highlighted the challenges involved in generating reliable clinical guidance, it has also provided an opportunity to address these challenges. RECENT FINDINGS: New research addressing drugs for COVID-19 is being produced at unprecedented rates. Incorporating this new knowledge into patient care can be daunting for the average clinician. In collaboration with the BMJ and MAGIC, the WHO has developed a living guideline initiative with the goal of providing rapid and trustworthy clinical guidance in response to practice-changing evidence. As new evidence becomes available, it is incorporated into a living network meta-analysis that informs these guidelines, which are iteratively updated. Until this point, the group has generated guidelines addressing the use of corticosteroids, remdesivir, hydroxychloroquine, lopinavir/ritonavir, and ivermectin for COVID-19. SUMMARY: We provide an example of how rapid and rigorous guidelines can be accomplished, even in the setting of a pandemic, capitalizing on expertise, large and dedicated teams, and focused scope. We highlight the benefits of multifaceted knowledge dissemination through multiple formats to ensure global dissemination and in order to maximize impact.
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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 | 0.269 | 0.742 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".