The methodological quality is insufficient in clinical practice guidelines in the context of COVID-19: systematic review
Bibliographic record
Abstract
OBJECTIVES: The number of published clinical practice guidelines related to COVID-19 has rapidly increased. This study explored if basic methodological standards of guideline development have been met in the published clinical practice guidelines related to COVID-19. STUDY DESIGN AND SETTING: Rapid systematic review from February 1 until April 27, 2020 using MEDLINE [PubMed], CINAHL [Ebsco], Trip and manual search, including all types of healthcare workers providing any kind of healthcare to any patient population in any setting. RESULTS: 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). The majority (156; 83%) was solely built on an informal expert consensus. A process for regular updates was described in 27 guidelines (14%). Patients were included in the development of only one guideline. CONCLUSION: Despite clear scope, most publications fell short of basic methodological standards of guideline development. Clinicians should use guidelines that include up-to-date information, were informed by stakeholder involvement, and employed rigorous methodologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.336 | 0.671 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".