Using population data to measure outcomes of care: the case of hip and knee replacements.
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence points to overall improvements in health-related quality of life after joint replacement for osteoarthritis. Some patients, however, do not appear to benefit from joint replacement. This study investigates health outcomes of patients who underwent hip or knee replacement surgery. METHODS: Linked survey and administrative data were used to compare the health-related quality of life of individuals who underwent surgery (surgical group) with that of their contemporaries who did not (comparison group), adjusting for other determinants of health. Weighted multivariate linear regression analyses were conducted. RESULTS: When the results were adjusted for other covariates known to be associated with health, the surgical group reported lower functional health (post-operative) than did the comparison group. Differences ranged from 6% lower functional health among hip replacement patients diagnosed with osteoarthritis to 21% lower functional health for those with hip fractures. Among surgical patients with osteoarthritis, co-morbid conditions and being underweight were associated with lower post-operative functional health. INTERPRETATION: This study is a unique application of linked data to the study of health outcomes of joint replacement at the population level. Outcomes of joint replacement differed by the initial diagnosis or reason for the surgery. For patients with osteoarthritis, poorer post-operative health outcomes were associated with co-morbidites and with being underweight.
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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 | 0.030 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".