National audit on the appropriateness of CT and MRI examinations in Luxembourg
Bibliographic record
Abstract
OBJECTIVES: In Luxembourg, the frequency of CT and MRI examinations per inhabitant is among the highest in Europe. A national audit was conducted to evaluate the appropriateness of CT and MRI examinations according to the national referral guidelines for medical imaging. METHODS: Three hundred and eighty-eight CT and 330 MRI requests corresponding to already performed examinations were provided by all radiology departments in Luxembourg. Four external radiologists evaluated the clinical elements for justification present in each request. They consensually assessed the appropriateness of each requested examination with regard to the national referral guidelines and their clinical experience. RESULTS: The appropriateness rate (AR) was higher for MRI requests than for CT requests (79% vs. 61%; p < 0.001). AR was higher for requests referred by medical specialists rather than by general practitioners, both for CT requests (70% vs. 37%; p < 0.001) and MRI requests (83% vs. 64%; p = 0.002). For CT, AR was higher when the requests concerned paediatric rather than adult patients (82% vs. 58%; p < 0.001), when the radiology departments were equipped with both CT and MRI units rather than with only CT units (65% vs. 47%, p = 0.004) and when the requests concerned head-neck (79%), chest (77%) and chest-abdominal-pelvic (81%) areas rather than spinal (28%), extremity (51%) and abdominal-pelvic (63%) areas (p < 0.001). CONCLUSIONS: The appropriateness of CT and MRI in Luxembourg is not satisfactory and collective efforts to improve should be continued. The focus should be on general practitioners and on spinal CT examinations.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".