Completeness of reporting of case reports in high‐impact medical journals
Bibliographic record
Abstract
INTRODUCTION: Case reports represent a relevant, timely and important study design in advancing medical scientific knowledge. They allow integration between clinical practice and clinical epidemiology. We aimed to assess the completeness of reporting (COR) of case reports published in high-impact journals. We assessed the COR of case reports using the CARE guidelines. MATERIALS AND METHODS: We selected three high-impact journals and one journal specialized in publishing case reports, in which we included all published case reports from July to December 2017. Median COR score was calculated per study, and CORs were compared between journals with and without endorsement of CARE guidelines. RESULTS: One hundred and fourteen case reports were included. Overall median COR was 81%, IQR [63%-96%]. Sections with the highest COR (84%-100%) were patient information, clinical findings, therapeutic intervention, follow-up and outcomes, discussion and informed consent. Sections with the lowest COR were title, keywords, timeline and patient perspective (2%-34%). COR was higher in journals endorsing in comparison to those not endorsing CARE guidelines (77% vs 65%), respectively, median difference = -12% 95% CI [-16% to -7%]. DISCUSSION: Overall completeness of case reports in included journals is high especially for CARE endorsing and dedicated journals but reporting of some items could be improved. Ongoing and future evaluations of endorsement status of reporting guidelines in medical journals should be assessed to improve completeness and reduce waste of clinical research, including case reports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Metaresearch 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 | 0.217 | 0.606 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".