Effect of patient characteristics on reported outcomes after total knee replacement
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of pre-intervention factors in patient-reported outcomes at 6 months post-operatively following total knee replacement. METHODS: A prospective observational study was carried out using two questionnaires sent to patients while they were on the waiting list for surgery: a generic questionnaire, the Medical Outcomes Study Short Form-36 (SF-36), and a specific questionnaire, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Six months after intervention, patients again received the same questionnaires. The dependent variables were the scores of the three domains of the WOMAC and the eight domains of the SF-36. RESULTS: We recruited 640 patients. The mean age was 71 yrs and 73.6% of the patients were females. The multivariate analysis, in which the pre-intervention scores for each domain were added as covariates, showed that the most significant pre-intervention predictors were the baseline scores of each domain. Besides that, the social support, low back pain and the baseline score of the mental health domain (SF-36) were the pre-intervention predictors in the three WOMAC domains. With regard to the SF-36 domains the main predictors were the baseline mental health score, comorbidities, low back pain and social support. CONCLUSIONS: The main predictor of outcome at 6 months post-operatively in all eleven domains was the pre-intervention score of each domain. Presence of social support, absence of low back pain and higher baseline SF-36 mental health score were related to the improvement in the health-related quality of life post-operatively.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".