The relationship between methodological quality and the use of retracted publications in evidence syntheses
Bibliographic record
Abstract
BACKGROUND: Evidence syntheses cite retracted publications. However, citation is not necessarily endorsement, as authors may be criticizing or refuting its findings. We investigated the sentiment of these citations-whether they were critical or supportive-and associations with the methodological quality of the evidence synthesis, reason for the retraction, and time between publication and retraction. METHODS: Using a sample of 286 evidence syntheses containing 324 citations to retracted publications in the field of pharmacy, we used AMSTAR-2 to assess methodological quality. We used scite.ai and a human screener to determine citation sentiment. We conducted a Pearson's chi-square test to assess associations between citation sentiment, methodological quality, and reason for retraction, and one-way ANOVAs to investigate association between time, methodological quality, and citation sentiment. RESULTS: Almost 70% of the evidence syntheses in our sample were of critically low quality. We found that these critically low-quality evidence syntheses were more associated with positive statements while high-quality evidence syntheses were more associated with negative citation of retracted publications. In our sample of 324 citations, 20.4% of citations to retracted publications noted that the publication had been retracted. CONCLUSION: The association between high-quality evidence syntheses and recognition of a publication's retracted status may indicate that best practices are sufficient. However, the volume of critically low-quality evidence syntheses ultimately perpetuates the citation of retracted publications with no indication of their retracted status. Strengthening journal requirements around the quality of evidence syntheses may lessen the inappropriate citation of retracted publications.
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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 | 0.557 | 0.892 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".