Evaluation of the reliability and usability of CARE‐Radiology: A descriptive‐analytic study
Bibliographic record
Abstract
AIM: The study aimed to evaluate the reliability and usability of the CARE-Radiology checklist in assessing radiological case reports and provide a basis for its broader adoption and optimization. METHODS: Ten randomly selected radiological case reports published in scientific journals in 2020 were evaluated using the CARE-Radiology checklist. Twenty-six experts from 10 countries were invited to independently assess all ten reports. The reliability of the checklist was measured using Fleiss' Kappa, and Cronbach's alpha coefficient. Usability was evaluated by recording the time taken to complete the assessments and requesting the evaluators to rate each item on a Likert scale for its easiness of use. RESULTS: The median time for evaluating one radiological case report was 15 min. The overall agreement among evaluators showed moderate reliability with a Kappa value of 0.47 and a Cronbach's alpha of 0.51. The mean compliance rate for the items of CARE-Radiology was 61.8%, with some items exceeding 90% compliance. Items related to abstracts and keywords had the lowest compliance rates. The evaluators found most items easy to understand, with a few exceptions. CONCLUSIONS: The CARE-Radiology checklist is relatively easy for researchers to use and understand. Continuous feedback is necessary for future revisions and updates, to enhance the effectiveness of the checklist, and to improve user experience.
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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 | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".