An overview of the characteristics and methodological standards across systematic reviews with Meta-analysis of efficacy/effectiveness of influenza antiviral drugs
Bibliographic record
Abstract
Influenza antiviral drugs remain controversial and it is not clear if conclusions on their efficacy/effectiveness are based on high quality systematic reviews (SRs). We systematically identified, critically appraised, and summarized the characteristics and adherence to methodological standards in SRs with meta-analysis of efficacy/effectiveness of influenza antiviral drugs for prevention and/or treatment of influenza. We searched MEDLINE, Embase, Scopus, CINAHL, Global Health, and CDSR for English-language SR publications up to July 2020. We summarized the characteristics, adherence to methodological standards and SR quality (AMSTAR 2). From a total 3,898 citations after removal of duplicates from all identified citations, we included 24 SRs. Seventy-five percent (n = 18) were of a critically low quality, 8% (n = 2) of a low quality, 17% (n = 4) of a moderate quality, and none were of a high quality. Seventeen percent (n = 4) were industry-funded, 4% (n = 1) coauthored by industry employee(s), and 33% (n = 8) commissioned by an organization or authority. Only 33% percent (n = 8) reported protocol registration, 4% (n = 1) reported collaboration with a knowledge synthesis librarian/information specialist, and 17% (n = 4) utilized a systematic review reporting checklist. The evidence suggests that SRs of efficacy/effectiveness of influenza antiviral drugs are mostly of critically low quality and do not follow current best SR practices. These findings are significant in view of the important role of SRs in decision-making and the controversies that surround the use of the influenza antiviral drugs. However, the findings should not be interpreted to mean curtailment/cessation of use of antiviral drugs for influenza.
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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 | 0.373 | 0.707 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.055 | 0.056 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".