Development and Internal Validation of Novel Risk Tools to Predict Subsequent Shoulder Surgery After Proximal Humerus Fractures
Bibliographic record
Abstract
OBJECTIVE: To (1) identify predictors of subsequent surgery after initial treatment of proximal humerus fractures (PHFs) and (2) generate valid risk prediction tools to predict subsequent surgery. METHODS: We identified patients ≥50 years with PHF from 2004 to 2015 using health data sets in Ontario, Canada. We used procedural codes to classify patients into treatment groups of (1) surgical fixation, (2) shoulder replacement, and (3) conservative. We used procedural and diagnosis codes to capture subsequent surgery within 2 years after fracture. We developed regression models for two-thirds of each group to identify predictors of subsequent surgery and the regression equations to develop risk tools to predict subsequent surgery. We used the final third of each cohort to evaluate the discriminative ability of the risk tools using c-statistics. RESULTS: We identified 20,897 patients with PHF, 2414 treated with fixation, 1065 with replacement, and 17,418 treated conservatively. Predictors of reoperation after fixation included bone grafting and nail or wire fixation versus plate fixation, whereas poor bone quality was associated with reoperation after initial replacement. In conservatively treated patients, more comorbidities were associated with subsequent surgery, whereas age 70+ and discharge home after presentation lowered the odds of subsequent surgery. The risk tools were able to discriminate with c-statistics of 0.75-0.88 (derivation) and 0.51-0.79 (validation). CONCLUSIONS: Our risk tools showed good to strong discriminative ability for patients treated conservatively and with fixation. These data may be used as the foundation to develop a clinically informative tool. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".