The reproducibility of assessing radiological reporting: studies from the development of the General Medical Council’s Performance Procedures
Bibliographic record
Abstract
OBJECTIVES: To investigate the reproducibility of peer ratings of consultant radiologists' reports, as part of the new General Medical Council (GMC) Performance Procedures. DESIGN: An evaluation protocol was piloted, used in a blocked, balanced, randomized generalizability analysis with three blocks of three judges (raters), each rating 30 reports from 10 radiologists, and re-rated to estimate intrarater reliability with conventional statistics (kappa). SETTING: Rating was performed at the Royal College of Radiologists. Volunteers were sampled from 23 departments of radiology in university teaching and district general hospitals. PARTICIPANTS: A nationally drawn non-random sample of 30 consultant radiologists contributing a total of 900 reports. Three trained and six non-trained judges were used in the rating analysis. RESULTS: A protocol was generated that was usable by judges. Generalizable results would be obtained with not less than three judges all rating the same 60 reports from a radiologist. CONCLUSIONS: Any assessment of performance of technical abilities in this field will need to use multiple assessors, basing judgements on an adequate sample of reports.
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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 | 0.375 | 0.696 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".