Early failures of total hip replacement: Effect of surgeon volume
Bibliographic record
Abstract
OBJECTIVE: To determine whether provider volume is associated with early failures following total hip replacement (THR) requiring revision. METHODS: Claims data were analyzed to follow a cohort of 57,488 Medicare beneficiaries who underwent elective primary THR in 1995-1996 in 3,044 hospitals in the US. Patients were followed through the end of 1999. Failure of primary THR was defined as a subsequent revision THR, as determined by International Classification of Diseases, Ninth Revision codes in hospital claims. Hospitals were stratified into 4 volume groups: low (<25 THRs/year), medium (26-50, 51-100 THRs/year), and high (>100 THRs/year). Low-volume surgeons were defined as those surgeons performing <12 elective primary THRs annually in the Medicare population. Associations between the rates of revision and surgeon volume were determined by hazard ratios from a proportional hazard model, with adjustment for hospital volume, patient age, poverty status, sex, and comorbidities. We also examined whether the effect of surgeon volume on revision rates differed between the first 18 months postoperatively and later time periods. RESULTS: Among 57,488 patients who had elective primary THR in 1995-1996, 2,537 (4.4%) had at least 1 revision THR by the end of 1999, with 1,437 (56.6%) of these revisions occurring within the first 18 months after the index primary THR. Median followup time was 47 months (range 0-54). Patients of high-volume surgeons were less likely to have revision THRs than patients of low-volume surgeons, regardless of hospital volume stratum. Further analysis revealed that the effect of surgeon volume on revisions was striking in the first 18 months after surgery but was not evident in the subsequent years. CONCLUSION: Patients of low-volume surgeons have higher rates of revision THR than patients of high-volume surgeons, particularly within the first 18 months postoperatively. Referring clinicians should consider including surgeon volume among the factors influencing their choice of surgeon for elective THR.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".