Impact of preoperative mental health status on functional outcome 1 year after total hip arthroplasty
Bibliographic record
Abstract
Background: The aim of this prospective cohort study was to determine the effect of preoperative mental health status on functional outcome 1 year after total hip arthroplasty (THA). Methods: Data were collected for 677 patients from a randomized controlled trial in Alberta who received primary THA between April 2005 and June 2006 (sex, age, body mass index [BMI], comorbidities, back pain and need for another lower limb arthroplasty procedure within 1 yr after surgery). The Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and 36-Item Short Form Health Survey (SF-36) mental health component were administered before surgery and 1 year after. We conducted multiple linear regression to determine the effect of mental health on the WOMAC score at 1 year. Results: The mean WOMAC and SF-36 mental health scores were significantly increased at 1 year (p < 0.001 and p = 0.01, respectively). There was a strong correlation between improvement in WOMAC score at 1 year and presurgery SF-36 mental health score (0.13, 95% confidence interval [CI] 0.06 to 0.2). Age (–0.34, 95% CI –0.45 to –0.24), obesity (–2.9, 95% CI –5.32 to –0.4), back pain (–5.75, 95% CI –8.04 to –3.46) and awaiting another joint arthroplasty operation (–6.18, 95% CI –8.9 to –3.47) had a negative impact on the WOMAC score. Conclusion: There was a strong correlation between presurgery mental health and the resolution of pain and improved functioning 1 year after THA. We recommend that patients receive appropriate counselling and, where appropriate, medical therapy before THA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".