Development and Validation of the New International Classification for Scapula Fractures
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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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 | 0.099 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".