Clinical practice guidelines and recommendations in the context of the COVID-19 pandemic: systematic review and critical appraisal
Bibliographic record
Abstract
Abstract Background The number of published clinical practice guidelines and recommendations related to SARS-CoV-2 infections causing COVID-19 has rapidly increased. However, insufficient consideration of appropriate methodologies in the guideline development could lead to misleading information, uncertainty among professionals, and potentially harmful actions for patients. Purpose Rapid systematic review of clinical practice guidelines and recommendations in the context of COVID-19 to explore if basic methodological standards of guideline development have been met. Data sources MEDLINE [PubMed], CINAHL [Ebsco], Trip and manual search; from Feb 1 st 2020 until April 27 th 2020. Study selection All types of healthcare workers providing any kind of healthcare to any patient population in any setting. Data extraction At least two reviewers independently extracted guideline characteristics, conducted critical appraisal according to The Appraisal of Guidelines for Research and Evaluation Instrument (AGREE II) and classified the guidelines using the Association of the Scientific Medical Societies (AWMF) Guidance Manual and Rules for Guideline Development . We plan six-month updates (living review). Data synthesis There were 1342 titles screened and 188 guidelines included. The highest average AGREE II domain score was 89% for scope and purpose , the lowest for rigor of development (25%). Only eight guidelines (4%) were based on a systematic literature search and a structured consensus process by representative experts (classified as the highest methodological quality, S3 according to AWMF). Patients were only included in the development of one guideline. A process for regular updates was described in 27 guidelines (14%). Limitations Methodological focus only. Conclusions Despite clear scope, most publications fell short of basic methodological standards of guideline development. Future research should monitor the evolving methodological quality of the guidelines and their updates over time. Registration/Publication The protocol was published at www.researchgate.net , DOI: 10.13140/RG.2.2.21293.51689. Preliminary results are publicly available on medRxiv.
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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 | 0.212 | 0.580 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.040 | 0.032 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".