A new morphological classification for greater tuberosity fractures of the proximal humerus
Bibliographic record
Abstract
In this study, we describe a morphological classification for greater tuberosity fractures of the proximal humerus. We divided these fractures into three types: avulsion, split and depression. We retrospectively reviewed all shoulder radiographs showing isolated greater tuberosity fractures in a Level I trauma centre between July 2007 and July 2012. We identified 199 cases where records and radiographs were reviewed and included 79 men and 120 women with a mean age of 58 years (23 to 96). The morphological classification was applied to the first 139 cases by three reviewers on two occasions using the Kappa statistic and compared with the AO and Neer classifications. The inter- and intra-observer reliability of the morphological classification was 0.73 to 0.77 and 0.69 to 0.86, respectively. This was superior to the Neer (0.31 to 0.35/0.54 to 0.63) and AO (0.30 to 0.32/0.59 to 0.65) classifications. The distribution of avulsion, split and depression type fractures was 39%, 41%, and 20%, respectively. This classification of greater tuberosity fractures is more reliable than the Neer or AO classifications. These distinct fracture morphologies are likely to have implications in terms of pathophysiology and surgical technique.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".