Which actionable statements qualify as good practice statements In Covid-19 guidelines? A systematic appraisal
Bibliographic record
Abstract
OBJECTIVES: To evaluate the development and quality of actionable statements that qualify as good practice statements (GPS) reported in COVID-19 guidelines. DESIGN AND SETTING: Systematic review . We searched MEDLINE, MedSci, China National Knowledge Infrastructure (CNKI), databases of Grading of Recommendations Assessment, Development and Evaluation (GRADE) Guidelines, NICE, WHO and Guidelines International Network (GIN) from March 2020 to September 2021. We included original or adapted recommendations addressing any COVID-19 topic. MAIN OUTCOME MEASURES: We used GRADE Working Group criteria for assessing the appropriateness of issuing a GPS: (1) clear and actionable; (2) rationale necessitating the message for healthcare practice; (3) practicality of systematically searching for evidence; (4) likely net positive consequences from implementing the GPS and (5) clear link to the indirect evidence. We assessed guideline quality using the Appraisal of Guidelines for Research and Evaluation II tool. RESULTS: 253 guidelines from 44 professional societies issued 3726 actionable statements. We classified 2375 (64%) as GPS; of which 27 (1%) were labelled as GPS by guideline developers. 5 (19%) were labelled as GPS by their authors but did not meet GPS criteria. Of the 2375 GPS, 85% were clear and actionable; 59% provided a rationale necessitating the message for healthcare practice, 24% reported the net positive consequences from implementing the GPS. Systematic collection of evidence was deemed impractical for 13% of the GPS, and 39% explained the chain of indirect evidence supporting GPS development. 173/2375 (7.3%) statements explicitly satisfied all five criteria. The guidelines' overall quality was poor regardless of the appropriateness of GPS development and labelling. CONCLUSIONS: Statements that qualify as GPS are common in COVID-19 guidelines but are characterised by unclear designation and development processes, and methodological weaknesses.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.271 | 0.660 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".