Accurate Measurement of Greater Tuberosity Displacement Without Computed Tomography
Bibliographic record
Abstract
INTRODUCTION: Residual displacement of greater tuberosity (GT) fractures has been shown to negatively affect shoulder function. However, accurate measurement of GT displacement remains a problem with errors up to 13 mm on plain radiography (XR). A new GT ratio for measuring fracture displacement on XR is described, validated, and correlated with computed tomography (CT) and surgical decision making. METHODS: A retrospective review of shoulder radiographs was performed from 2007 to 2010 to identify all cases of isolated GT fractures with both XR and CT. The GT ratio was performed on all XR and correlated with superior GT displacement measured on CT. The GT ratio was then tested for accuracy of surgical decision using 5-mm superior displacement on CT as the cutoff. Finally, the inter- and intraobserver reliabilities of the GT ratio were calculated and compared with the Neer and Arbeitsgemeinschaft fur Osteosynthesefragen (AO) classifications. RESULTS: Forty cases of acute GT fractures with XR and CT were identified. The GT ratio correlated very well with superior displacement on CT (Pearson correlation = 0.852, P < 0.01) and accurately classified GT fractures as "surgical" (n = 9, 23%) or "nonsurgical" (n = 31, 77%). GT ratios ≤0.00 were nonsurgical, ≥0.50 were surgical, and 0.00-0.50 warranted further imaging (P < 0.01). The GT ratio performed as well as or better than the AO and Neer classifications for inter- and intraobserver reliabilities. CONCLUSIONS: The GT ratio described in this study correlates very well with CT for superior GT fracture displacement. It involves significantly less radiation and accurately classifies GT fractures as nonsurgical (ratio < 0.00), surgical (ratio > 0.50), or as benefiting from further imaging (0.00-0.50). It performs as well or better than the Neer or AO classification. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".