<p>Retraction of COVID-19 Pharmacoepidemiology Research Could Have Been Avoided by Effective Use of Reporting Guidelines</p>
Bibliographic record
Abstract
Introduction: Two recent high-profile publications (and subsequent retractions) of pharmacoepidemiology studies reporting the effectiveness and risk of hydroxychloroquine in COVID-19 patients received international media attention. Transparent and complete reporting of these studies could have provided peer reviewers and editors with sufficient information to question the methods used and the validity of results. Since these studies used routinely collected health data, the guidelines for the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) should have been applied to ensure complete reporting of the research. Methods: We evaluated the two retracted articles for completeness of reporting using the RECORD for Pharmacoepidemiology (RECORD-PE) checklist, which includes the checklists for the STengthening the Reporting of OBservational studies in Epidemiology (STROBE) and RECORD. We compared the proportion of STROBE, RECORD and RECORD-PE items adequately reported using Chi-squared statistics. Results: In the article published by The Lancet , 29 of 34 STROBE items (85.3%) were adequately reported, compared with 3.5 of 13 RECORD items (26.9%) and 9.5 of 15 RECORD-PE items (63.3%)(χ 2 = 14.839, P < 0.001). Similarly, the article published in NEJM reported 24 of 34 STROBE items (70.6%), two of 13 RECORD items (15.4%), and 7.5 of 15 RECORD-PE items (50.0%) (χ 2 = 11.668, P = 0.003). Important aspects of the methods unique to research using routinely collected health data were not reported, including variables used to identify exposure, outcome and confounders, validation of the coding or algorithms, a description of the underlying database population and the accuracy of data linkage methods. Discussion: While STROBE items were generally adequately reported, RECORD and RECORD-PE items were not. Reporting guidelines should be effectively implemented in order for transparency and completeness of research manuscripts, allowing for adequate evaluation by editors and peer reviewers. Keywords: COVID-19, journalology, peer review, pharmacoepidemiology, reporting guidelines, routinely collected health data
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How this classification was reachedexpand
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: Reporting · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchResearch integrity Domain: Reporting · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.388 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.040 | 0.027 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".