Development and Validation of the New International Classification for Scapula Fractures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Multiple scapula classification systems exist in the literature and were developed using a consensus approach with one or several experts agreeing on a classification without stringent validation. None have gained widespread acceptance. A decision was made by the OTA classification committee and the AO Classification Advisory Group to collaborate on the development of a new validated classification system capable of addressing the limitations of the existing systems. METHODS: A feedback validation process through 4 iterations of revised classifications on radiographs and computed tomography (CT) scans was used. Statistical analyses calculated the proportion of agreement among surgeons and kappa statistics for the assessment of coding reliability. Estimates of classification accuracy were obtained using latent class modeling. RESULTS: Fractures of the scapular neck are rare injuries and were difficult to define and diagnose with kappa values ranging from 0.28 to 0.40. Although fossa fractures could be identified on plain radiographs, specific fracture patterns could only be classified with CT scans. The new classification divides the scapula into 3 segments: fossa, body, and processes. The validation has shown that the classification can be reliable using plain radiographs (kappa 0.66), increasing to kappa of 0.78 when CT scans were added. CONCLUSIONS: This basic coding system allows clinicians to describe and classify scapula fractures with a reasonable degree of reliability. This validated classification that has resulted from this process has been accepted by a disparate group of orthopaedic traumatologists as a better option for clinical communication and research documentation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 it