The game has changed… but it still needs to be played: the role of imaging tests using ionising radiation in the practice of sports medicine
Bibliographic record
Abstract
The editorial by Orchard et al 1 is a timely reminder of the mounting evidence of the negative bioeffects of imaging studies utilising ionising radiation in paediatric populations. This applies especially to CT and nuclear medicine, in which the doses are higher than X-rays, and which are much more commonly performed than in fluoroscopy. As the authors point out, these considerations are highly relevant to the practice of sports medicine, as the patients are often young, and bony injuries are commonly investigated using CT and nuclear medicine. So how are clinicians to integrate data from these increasingly well-designed studies2 into recommendations that will still allow accurate and timely diagnosis of sports-related pathology? As a professor of musculoskeletal radiology, I share the following thoughts: There have been a number of studies that correctly point out the need …
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.038 | 0.049 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".