Classifying health-related quality of life outcomes of total hip arthroplasty
Bibliographic record
Abstract
BACKGROUND: Primary total hip arthroplasty (THA) is an effective treatment for hip osteoarthritis, assessed by whatever distribution-based measures of responsiveness. Yet, the group level evaluation has provided very little evidence contributes to our understanding of the large variation of treatment outcome. The objective is to develop criteria that classify individual treatment health related quality of life (HRQOL) outcome after primary THA, adjusted by preoperative scores. METHODS: We prospectively measured 147 patients' disease specific HRQOL on the date of consultation and 12 months post operation by Western Ontario McMaster Universities Osteoarthritis Index (WOMAC). Regression models were used to determine the "expected" outcome for a certain individual baseline score. The ceiling effect of WOMAC measurement is addressed by implementing a left-censoring method. RESULTS: The classification criteria are chosen to be the lower boundary of the 95% confidence interval (CI) of the estimated median from the regression. The robustness of the classification criteria was demonstrated using the Monte-Carlo simulation. CONCLUSION: The classification criteria are robust and can be applied in general orthopaedic research when the sample size is reasonable large (over 500).
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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 | 0.010 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".