Provider volume and other predictors of outcome after total knee arthroplasty: a population study in Ontario.
Bibliographic record
Abstract
INTRODUCTION: Because of rationing of the limited pool of health care resources, access to total knee arthroplasty (TKA) is limited, but investigation of variables that predict complications, length of hospital stay, cost and outcomes of TKA may allow us to optimize the available resources. The objective of this study was to examine the effect of various factors on complication rates after TKA in patients managed in Ontario. METHODS: Patients who had undergone an elective TKA between 1993 and 1996, as captured in the Canadian Institute for Health Information (CIHI) database, formed the study cohort. The CIHI dataset was used to obtain information regarding in-hospital complications, hospital length of stay, revision rates, infection rates and mortality. Generalized estimating linear or logistic regression equations were used to model outcomes as a function of age, gender, comorbidity, diagnosis and provider volume. RESULTS: During the study period, 14,352 patients in Ontario underwent TKA. Mortality at 3 months was associated with patient age, gender and comorbidity. There was no association between provider volume and mortality or the infection rate. Higher revision rates at 1 and 3 years were significantly associated with lower patient age and low hospital volume (p < 0.05). Hospitals in which fewer than 48 TKA procedures were done per year (< 40th percentile) had 2.2-fold greater 1-year revision rates than hospitals performing more than 113 TKAs annually (> 80th percentile). Complications during admission were associated with increased patient age and comorbidity, and higher hospital volume. Longer hospital stay was associated with female gender, increasing patient comorbidity and age, and lower provider volume. Surgeons who performed fewer than 14 TKAs annually (< 40th percentile) kept patients in hospital an average of 1.4 days longer than surgeons performing more than 42 TKAs annually (> 80th percentile). CONCLUSIONS: Patient variables significantly affect the rate of complications. Age, sex and comorbidity were significant predictors of complications, length of hospital stay and mortality after TKA. Although low surgeon volume was related to longer hospital stay, there was no association between surgeon volume and complication rates. The increased early revision rate for low-volume hospitals demands further study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".