Quality and methodology of clinical practice guidelines on antiviral pharmacotherapy for COVID-19 during the early phase of the pandemic
Bibliographic record
Abstract
Background: Despite availability of reliable guidelines development methods, the risk of producing less reliable documents may be higher when the guidelines are developed rapidly. Methods: We performed the search for guidelines, published before the results of any randomized controlled trials of COVID-19 treatment were available. The quality of the guidelines was assessed using the AGREE II-Global Rating Scale Instrument and series of dichotomous criteria based on the domain 3 of the AGREE II tool. We analyzed variables associated with the presence of recommendations for antiviral therapy for SARS-CoV-2. Results: The analysis included 40 publications. The median of quality of documents assessed with the AGREE II-GRS tool (overall quality assessment on a scale ranging from 1-7) was 2.0 (IQR 1.5–2.5). Most documents did not fulfill the rigour of guideline development quality criteria. Overall, 62.5% of documents provided recommendations for the use of antiviral medications despite apparent lack of sufficient evidence supporting such treatments. Documents that contained recommendations supporting antiviral drug use tended to be of lower quality than those without such recommendations. Of the included documents, 75% were not updated within the 2 months after the publication of the first randomized controlled trial on COVID-19 antiviral therapy. Conclusions: Most guidelines or guidance documents published during the early phase of the COVID-19 pandemic were of poor quality, contained recommendations for the use of antiviral therapy for SARS-CoV-2 infection despite only very low
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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 | 0.440 | 0.764 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".