Associations between provider and hospital volumes and postoperative mortality following total hip arthroplasty in New Brunswick: results from a provincial-level cohort study
Bibliographic record
Abstract
BACKGROUND: Several international studies have reported negative associations between hospital and/or provider volume and risk of postoperative death following total hip arthroplasty (THA). The only Canadian studies to report on this have been based in Ontario and have found no such association. We describe associations between postoperative deaths following THA and provider caseload volume, also adjusted for hospital volume, in a population-based cohort in New Brunswick. METHODS: Our analyses are based on hospital discharge abstract data linked to vital statistics and to patient registry data. We considered all first known admissions for THA in New Brunswick between Jan. 1, 2007, and Dec. 31, 2013. Provider volume was defined as total THAs performed over the preceding 2 years. We fit logistic regression models to identify odds of dying within 30 and 90 days according to provider caseload volume adjusted for selected personal and contextual characteristics. RESULTS: About 7095 patients were admitted for THA in New Brunswick over the 7-year study period and 170 died within 30 days. We found no associations with provider volume and postoperative mortality in any of our models. Adjustment for contextual characteristics or hospital volume had no effects on this association. CONCLUSION: Our results suggest that patients admitted for hip replacements in New Brunswick can expect to have similar risk of death regardless of whether they are admitted to see a provider with high or low THA volumes and of whether they are admitted to the province's larger or smaller hospitals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".