Involvement of the proximal radial ulnar joint in partial radial head fractures: a novel three-dimensional computed tomography scan evaluation method
Bibliographic record
Abstract
BACKGROUND: Partial radial head fractures (PRHF) can involve the proximal radioulnar joint (PRUJ) or be restricted to the 'safe zone' (SZ) during forearm rotation. The objective of the present study was to develop an assessment method for PRUJ involvement in radial head fractures using axial computed tomography (CT) scans. METHODS: The area of the radial head in contact with the PRUJ zone was identified, and defined on 18 cadaveric elbows CT scans; the quantitative relationship between PRUJ zone and radial tuberosity was established. Then, four evaluators validated it on PRHF CT scan axial views, classifying the fractures as involving the PRUJ or not. RESULTS: Using the radial tuberosity as the 0° of a 360° circle, the SZ was within 108° to 212° clockwise for a right elbow and counter clockwise for the left elbow. Fifty-five consecutive (30 men, 25 women, mean age of 49 years) partial radial head fracture CT scans were classified: four in the SZ only, three in the PRUJ zone and 48 in both the PRUJ and SZ. The kappa for the inter- and intra-observer agreement was 0.517 and 0.881, respectively. CONCLUSIONS: Ninety-three percent of partial radial head fractures will involve the PRUJ and the geometric model developed allows their classification, potentially helping surgeons decide on optimal treatment. LEVEL OF EVIDENCE: Retrospective basic science study. Level III: anatomic study, imaging.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".