Consistency of recommendations and methodological quality of guidelines for the diagnosis and treatment of COVID‐19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Since the beginning of the COVID-19 epidemic, a large number of guidelines on diagnosis and treatment of COVID-19 have been developed, but the quality of those guidelines and the consistency of recommendations are unclear. The objective of this study is to evaluate the quality of the diagnosis and treatment guidelines on COVID-19 and analyze the consistency of the recommendations of these guidelines. METHODS: We searched for guidelines on diagnosis and/or treatment of COVID-19 through PubMed, CBM, CNKI, and WanFang Data, from January 1, 2020 to August 31, 2020. In addition, we also searched official websites of the US CDC, European CDC and WHO, and some guideline collection databases. We included diagnosis and/or treatment guidelines for COVID-19, including rapid advice guidelines and interim guidelines. Two trained researchers independently extracted data and four trained researchers evaluated the quality of the guidelines using the AGREE II instruments. We extracted information on the basic characteristics of the guidelines, guideline development process, and the recommendations. We described the consistency of the direction of recommendations for treatment and diagnosis of COVID-19 across the included guidelines. RESULTS: A total of 37 guidelines were included. Most included guidelines were assessed as low quality, with only one of the six domains of AGREE II (clarity of presentation) having a mean score above 50%. The mean scores of three domains (stakeholder involvement, the rigor of development and applicability) were all below 30%. The recommendations on diagnosis and treatment were to some extent consistent between the included guidelines. Computed tomography (CT), X-rays, lung ultrasound, RT-PCR, and routine blood tests were the most commonly recommended methods for COVID-19 diagnosis. Thirty guidelines were on the treatment of COVID-19. The recommended forms of treatment included supportive care, antiviral therapy, glucocorticoid therapy, antibiotics, immunoglobulin, extracorporeal membrane oxygenation (ECMO), convalescent plasma, and psychotherapy. CONCLUSIONS: The methodological quality of currently available diagnosis and treatment guidelines for COVID-19 is low. The diagnosis and treatment recommendations between the included guidelines are highly consistent. The main diagnostic methods for COVID-19 are RT-PCR and CT, with ultrasound as a potential diagnostic tool. As there is no effective treatment against COVID-19 yet, supportive therapy is at the moment the most important treatment option.
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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.326 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it