Delays Worsen Quality of Life Outcome of Primary Total Hip Arthroplasty
Bibliographic record
Abstract
Although there are indications of health status deterioration for patients while waiting for elective total hip arthroplasties, controversy exists regarding the effect of waiting on postoperative outcomes. We hypothesized that longer waiting times are detrimental to achieving the full benefit of surgery. We prospectively examined 201 patients with osteoarthritis who were on the waiting list for primary total hip arthroplasties. The Western Ontario and McMaster Universities Osteoarthritis Index questionnaire was used to assess patients at surgical consultation (preoperative) and 1 year postoperative. The study included regression models to determine the expected outcome for an individual's preoperative score. Logistic regression models were used to assess the relationship between waiting time and the probability of a better than expected outcome. We found that the odds of achieving a better than expected postoperative functional outcome decreased by 8% for each month on the waiting list. Expedited access resulted in a larger proportion of patients with better than expected function 12 months after surgery. Level of Evidence: Prognostic study, Level I (high quality prospective study [all patients were enrolled at the same point in their disease with ≥ 80% followup of enrolled patients]). See the Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".