CARE guidelines for case reports: explanation and elaboration document. Translation into Russian
Bibliographic record
Abstract
BACKGROUND: Well-written and transparent case reports (1) reveal early signals of potential benefits, harms, and information on the use of resources; (2) provide information for clinical research and clinical practice guidelines, and (3) inform medical education. High-quality case reports are more likely when authors follow reporting guidelines. During 20112012, a group of clinicians, researchers, and journal editors developed recommendations for the accurate reporting of information in case reports that resulted in the CARE (CAse REport) Statement and Checklist. They were presented at the 2013 International Congress on Peer Review and Biomedical Publication, have been endorsed by multiple medical journals, and translated into nine languages. OBJECTIVES: This explanation and elaboration document has the objective to increase the use and dissemination of the CARE Checklist in writing and publishing case reports. ARTICLE DESIGN AND SETTING: Each item from the CARE Checklist is explained and accompanied by published examples. The explanations and examples in this document are designed to support the writing of high-quality case reports by authors and their critical appraisal by editors, peer reviewers, and readers. RESULTS AND CONCLUSION: This article and the 2013 CARE Statement and Checklist, available from the CARE website [www.care-statement.org] and the EQUATOR Network [www.equator-network.org], are resources for improving the completeness and transparency of case reports. SOURCE: This article is a translation of the original paper CARE guidelines for case reports: explanation and elaboration document in the Journal of Clinical Epidemiology (doi: 10.1016/j.jclinepi.2017.04.026), prepared under the permission of the copyright holder (Elsevier Inc.), with supervision from the Scientific Editor by Professor E.G. Starostina, MD, PhD (translator) (Moscow, Russia).
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How this classification was reachedexpand
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 | 0.028 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.042 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".