A higher reoperation rate following arthroplasty for failed fixation <i>versus</i> primary arthroplasty for the treatment of proximal humeral fractures
Bibliographic record
Abstract
Aims To compare complication-related reoperation rates following primary arthroplasty for proximal humerus fractures (PHFs) versus secondary arthroplasty for failed open reduction and internal fixation (ORIF). Patients and Methods We identified patients aged 50 years and over, who sustained a PHF between 2004 and 2015, from linkable datasets. We used intervention codes to identify patients treated with initial ORIF or arthroplasty, and those treated with ORIF who returned for revision arthroplasty within two years. We used multilevel logistic regression to compare reoperations between groups. Results We identified 1624 patients who underwent initial arthroplasty for PHF, and 98 patients who underwent secondary arthroplasty following failed ORIF. In total, 72 patients (4.4%) in the primary arthroplasty group had a reoperation within two years following arthroplasty, compared with 19 patients (19.4%) in the revision arthroplasty group. This difference was significantly different (p < 0.001) after covariable adjustment. Conclusion The number of reoperations following arthroplasty for failed ORIF of PHF is significantly higher compared with primary arthroplasty. This suggests that primary arthroplasty may be a better choice for patients whose prognostic factors suggest a high reoperation rate following ORIF. Prospective clinical studies are required to confirm these findings. Cite this article: Bone Joint J 2019;101-B:1272–1279
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".