Completeness of reporting for COVID-19 case reports, January to April 2020: a meta-epidemiologic study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The quality of case reports, which are often the first reported evidence for a disease, may be negatively affected by a rush to publication early in a pandemic. We aimed to determine the completeness of reporting (COR) for case reports published on coronavirus disease 2019 (COVID-19). METHODS: We conducted a systematic search of the PubMed database for all single-patient case reports of confirmed COVID-19 published from Jan. 1 to Apr. 24, 2020. All included case reports were assessed for adherence to the CARE (Case Report) 31-item checklist, which was used to create a composite COR score. The primary outcome was the mean COR score assessed by 2 independent raters. Secondary outcomes included whether there was a change in overall COR score with certain publication factors (e.g., publication date) and whether there was a linear relation between COR and citation count and between COR scores and social media attention. RESULTS: Our search identified 196 studies that were published in 114 unique journals. We found that the overall mean COR score was 54.4%. No one case report included all of the 31 CARE checklist items. There was no significant correlation between COR with either citation count or social media attention. INTERPRETATION: We found that the overall COR for case reports on COVID-19 was poor. We suggest that journals adopt common case-reporting standards to improve reporting quality.
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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 | MetaresearchMeta-epidemiology (broad) Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | 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 | 0.009 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it