Protocol for developing the reporting guidelines for radiological case reports: Case Report for Radiology statement
Bibliographic record
Abstract
Background: In radiology, case reports play an important role in the presentation of a new disease or an unusual form of a common disease using radiological images. Radiology practitioners can refer to the CAse REport (CARE) statement to write and improve the quality of case reports; however, some CARE items are not applicable to the field of radiology. This protocol seeks to describe the methods and processes used to develop CARE extensions for radiology. Methods: We plan to extend the existing CARE guidelines to radiological case reports. We will follow the steps recommended by the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network to develop the CAse Report for Radiology (CARR) statement and checklist for the reporting of case reports. The working group will constitute a multidisciplinary international team of experts, including methodologists, content experts (radiologists and clinicians), journal editors, and possibly consumer representatives. We will discuss and generate a list of initial items based on the CARE statement. Two to three rounds of the Delphi survey will be administered and an online consensus meeting will be held to reach a consensus and develop the final CARR checklist. The full reporting guidelines should be finalized within 1.5 years. Discussion: The annual number of published radiological case reports has increased over the past 20 years; however, the quality of reporting still needs to be improved. Our protocol envisages the process and methodology for the development of the CARR guidelines, which we anticipate will be available soon and will help radiology practitioners. Trial Registration: We have registered the protocol on the EQUATOR network (https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-observational-studies/#CARR).
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
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".