A quality evaluation of guidelines on five different viruses causing public health emergencies of international concern
Bibliographic record
Abstract
This project aims to evaluate the methods and reporting quality of practice guidelines of five different viruses that have caused Public Health Emergencies of International Concern (PHEIC) over 20 past years: the severe acute respiratory syndrome coronavirus (SARS-CoV), Ebola virus, Middle East respiratory syndrome coronavirus (MERS-CoV), Zika virus and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We systematically searched databases, guideline websites and government health agency websites from their inception to February 02, 2020 to extract practice guidelines for SARS-CoV, Ebola virus, MERS-CoV, Zika virus, SARS-CoV-2 and the diseases they caused. The literature was screened independently by four researchers. Then, fifteen researchers evaluated the quality of included guidelines using the AGREE-II (Appraisal of Guidelines for Research and Evaluation II, for methodological quality) instrument and RIGHT (Reporting Items for practice Guidelines in Healthcare, for reporting quality) statement. Finally, a total of 81 guidelines were included, including 21 SARS-CoV guidelines, 11 Ebola virus (EBOV) guidelines, 9 MERS-CoV guidelines, 10 Zika Virus guidelines and 30 SARS-CoV-2 guidelines. The evaluation of the methodological quality indicated that the mean scores of each domain for guidelines of each virus were all below 60%, the scores for guidelines in the domains of "clarity of presentation" being the highest and in the "editorial independence" lowest. The mean reporting rate of each domain for guidelines of each virus was also less than 60%: the reporting rates for the domain "background" were highest, and for the domain "funding and interests" lowest. The methodological and reporting quality of the practice guidelines for SARS-CoV, Ebola virus, MERS-CoV, Zika virus and SARS-CoV-2 guidelines tend to be low. We recommend to follow evidence-based methodology and the RIGHT statement on reporting when developing guidelines.
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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 | 0.556 | 0.835 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.030 | 0.028 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".