Completeness of reporting for COVID-19 case reports, January to April 2020: a meta-epidemiologic study
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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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 | 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 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 | 0.095 | 0.309 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.025 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| 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, 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".