Scoping review of COVID-19-related systematic reviews and meta-analyses: can we really have confidence in their results?
Bibliographic record
Abstract
AIM: The aim of this study was to systematically appraise the quality of a sample of COVID-19-related systematic reviews (SRs) and discuss internal validity threats affecting the COVID-19 body of evidence. DESIGN: We conducted a scoping review of the literature. SRs with or without meta-analysis (MA) that evaluated clinical data, outcomes or treatments for patients with COVID-19 were included. MAIN OUTCOME MEASURES: We extracted quality characteristics guided by A Measurement Tool to Assess Systematic Reviews-2 to calculate a qualitative score. Complementary evaluation of the most prominent published limitations affecting the COVID-19 body of evidence was performed. RESULTS: A total of 63 SRs were included. The majority were judged as a critically low methodological quality. Most of the studies were not guided by a pre-established protocol (39, 62%). More than half (39, 62%) failed to address risk of bias when interpreting their results. A comprehensive literature search strategy was reported in most SRs (54, 86%). Appropriate use of statistical methods was evident in nearly all SRs with MAs (39, 95%). Only 16 (33%) studies recognised heterogeneity in the definition of severe COVID-19 as a limitation of the study, and 15 (24%) recognised repeated patient populations as a limitation. CONCLUSION: The methodological and reporting quality of current COVID-19 SR is far from optimal. In addition, most of the current SRs fail to address relevant threats to their internal validity, including repeated patients and heterogeneity in the definition of severe COVID-19. Adherence to proper study design and peer-review practices must remain to mitigate current limitations.
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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 | 0.392 | 0.801 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.022 |
| Bibliometrics | 0.037 | 0.029 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".