Employment status and personal characteristics in patients awaiting hip-replacement surgery.
Bibliographic record
Abstract
BACKGROUND: Total hip arthroplasty (THA) is a cost-effective surgical intervention that substantially improves quality of life. Recent advances have broadened the indications to include younger, working-age patients. Despite these benefits, there are often long waits for this procedure in Canada. Furthermore, there exists little documentation of the ability of patients waiting for THA to maintain employment or perform their occupational duties. METHODS: I prospectively identified patients younger than 65 years from a primary hip-replacement surgery waiting list. The study coordinator contacted patients by phone and asked them to participate; if they agreed, we mailed them a validated questionnaire. To compare working with nonworking patients, I used univariate analysis and logistic regression modeling. RESULTS: A total of 84 of the 100 patients who agreed to participate returned the questionnaire. While awaiting THA, 20% of patients who considered themselves to be in the workforce were off work owing to their hip conditions. Work cessation resulted in a median drop in income of $15,000 CDN and forgone tax revenues of $3800. Poor hip function was related to both lowered productivity and work cessation before surgery. Patients with an Oxford 12 hip score of 50 or worse appeared to have about a 50% chance of stopping work before THA, whereas those with a score of 40 or better appeared to have only a 10% chance of stopping work. CONCLUSION: About 20% of patients in the workforce who are awaiting THA are off work owing to their hip conditions while on the waiting list. Poor hip function is associated with work cessation and decreased productivity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".