COVID-19 retracted publications on retraction watch: A systematic survey of their pre-prints and citations
Bibliographic record
Abstract
Background: Studies related to the coronavirus disease 2019 (COVID-19) were frequently published as pre-prints prior to undergoing peer-review. However, several publications were later retracted due to ethical concerns or study misconduct. Although these studies have been retracted, the availability of their corresponding pre-prints has never been formally investigated, and may result in the spread of misinformation if they are being used to inform decision-making. Methods: Our objective was to conduct a systematic survey of retracted COVID-19 publications listed on the Retraction Watch database as of August 15th, 2021. We assessed the availability of corresponding pre-prints for retracted publications, and documented the number of citations and online views. Results: Our study included 140 retracted COVID-19 publications, and we could not retrieve corresponding pre-prints for 132 retracted publications in our study (94%). Although we were unable to find the majority of pre-prints, they had already been disseminated, with a maximal citation count of 593 and Altmetric score of 558,928. Conclusion: While it is reassuring that most corresponding pre-prints could not be retrieved, our study highlights the need for online platforms and journals to employ quality assurance methods to prevent the spread of misinformation through citation of retracted papers.
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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 | MetaresearchResearch integrityBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchBibliometricsResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, 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".