Identification of patients with suboptimal results after hip arthroplasty: development of a preliminary prediction algorithm
Bibliographic record
Abstract
BACKGROUND: The ability to predict preoperatively the identity of patients undergoing hip arthroplasty at risk of suboptimal outcomes could help implement interventions targeted at improving surgical results. The objective was to develop a preliminary prediction algorithm (PA) allowing the identification of patients at risk of unsatisfactory outcomes one to two years following hip arthroplasty. METHODS: Retrospective data on a cohort of 265 patients having undergone primary unilateral hip replacement (188 total arthroplasties and 77 resurfacing arthroplasties) from 2004 to 2010 were collected from our arthroplasty database. Hip pain and function, as measured by the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were collected, as well as self-reported hip joint perception after surgery. Demographic and clinical variables recorded at the time of the surgery were considered as potential predictors. Patients were considered as having a suboptimal surgical outcome if they were in the worst quartile of the postoperative total WOMAC score and perceived their operated hip as artificial with minimal or major limitations. The PA was developed using recursive partitioning. RESULTS: Mean postoperative surgical follow-up was 446 ± 171 days. Forty patients (15.1 %) had a postoperative total WOMAC score in the worst quartile (>11.5/100) and perceived their joint as artificial with minimal or major restrictions. A PA consisting of the following variables achieved the most acceptable level of prediction: gender, age at the time of surgery, body mass index (BMI), and three items of the preoperative WOMAC (degree of pain with walking on a flat surface and during the night as well as degree of difficulty with putting socks or stockings). The rule had a sensitivity of 75.0 % (95 % CI: 59.8-85.8), a specificity of 77.8 % (95 % CI: 71.9-82.7), a positive predictive value of 37.5 % (95 % CI: 27.7-48.5), a negative predictive value of 94.6 % (95 % CI: 90.3-97.0) and positive and negative likelihood ratios of 3.38 (95 % CI: 2.49-4.57) and 0.34 (95 % CI: 0.19-0.55) respectively. CONCLUSIONS: The preliminary PA shows promising results at identifying patients at risk of significant functional limitations, increased pain and inadequate joint perception after hip arthroplasty. Clinical use should not be implemented before additional validation and refining.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".