Factors influencing surgeons' decisions in the indication for total joint replacement in hip osteoarthritis in real life
Bibliographic record
Abstract
OBJECTIVE: To evaluate factors influencing orthopedic surgeons' decision in daily practice to recommend or not recommend total hip arthroplasty (THA) in patients with hip osteoarthritis (OA). METHODS: General practitioners and rheumatologists were asked to prospectively include 1 patient with hip OA for whom a consultation with an orthopedic surgeon was planned to determine whether or not THA was indicated. The following variables were obtained: age, sex, occupational status, body mass index, comorbidities, duration of hip OA, patient's global assessment, Western Ontario and McMaster Universities Osteoarthritis Index pain and functioning subscale scores, New Zealand score, quality of life, and structural parameters on radiographs. The surgeon's decision was obtained by followup questionnaires. Statistical analysis evaluated potential predictors of the surgeon's decision (indication for THA within the next 6 months, yes or no) using univariate and multivariate analysis. RESULTS: A total of 558 patients were included (249 men, 300 women, mean age 68.4 years, mean disease duration 4.9 years). The surgeon's decision, available for 486 patients, was to prescribe THA in 60.7% of patients. On multivariate analysis, the variables related to the surgeon's decision were the presence or absence of severe cardiovascular disease, Short Form 12 physical subscale score, and amount of joint space narrowing. CONCLUSION: While the amount of structural degradation is only slightly or not at all taken into account in numerous criteria and/or recommendations on indications for THA, it is an independent predictor of the surgeon's decision in daily practice. Such a discrepancy should be evaluated and understood in further studies.
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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 | 0.023 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".