Impact of industry sponsorship on the quality of systematic reviews of vaccines: a cross-sectional analysis of studies published from 2016 to 2019
Bibliographic record
Abstract
BACKGROUND: Systematic reviews (SRs) provide the highest level of evidence and inform evidence-based decision making in health care. Earlier studies found association with industry to be negatively associated with methodological quality of SRs. However, this has not been investigated in SRs on vaccines. METHODS: We performed a systematic literature search using MEDLINE and EMBASE in March 2020. The results were restricted to those published between 2016 and 2019 with no language restrictions. Study characteristics were extracted by one person and checked by an experienced reviewer. The methodological quality of the SRs was assessed with the AMSTAR 2 tool by multiple reviewers after a calibration exercise was performed. A summary score for each SR was calculated. The Mann-Whitney U test and Fisher's exact test were performed to compare both groups. RESULTS: Out of 185 SRs that met all inclusion criteria, 27 SRs were industry funded. Those were matched with 30 non-industry funded SRs resulting in a total sample size of 57. The mean AMSTAR 2 summary score across all SRs was 0.49. Overall, the median AMSTAR 2 summary score was higher for the non-industry funded SRs than for the industry-funded SRs (0.62 vs. 0.36; p < .00001). Lower ratings for industry funded SRs were consistent across all but one AMSTAR 2 item, though significantly lower only for three specific items. CONCLUSION: The methodological quality of SRs in vaccination is comparable to SRs in other fields, while it is still suboptimal. We are not able to provide a satisfactory explanation why industry funded SRs had a lower methodological quality than non-industry funded SRs over recent years. Industry funding is an important indicator of methodological quality for vaccine SRs and should be carefully considered when appraising SR quality.
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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 | 0.208 | 0.520 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".