The functional and financial impact of isolated and recurrent dislocation after total hip arthroplasty
Bibliographic record
Abstract
The aims of this study were to determine the functional impact and financial burden of isolated and recurrent dislocation after total hip arthroplasty (THA). Our secondary goal was to determine whether there was a difference between patients who were treated non-operatively and those who were treated operatively. We retrospectively reviewed 71 patients who had suffered dislocation of a primary THA. Their mean age was 67 years (41 to 92) and the mean follow-up was 3.8 years (2.1 to 8.2). Because patients with recurrent dislocation were three times more likely to undergo operative treatment (p < 0.0001), they ultimately had a significantly higher mean Harris Hip Score (HHS) (p = 0.0001), lower mean Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores (p = 0.001) and a higher mean SF-12 score (p < 0.0001) than patients with a single dislocation. Likewise, those who underwent operative treatment had a higher mean HHS (p < 0.0001), lower mean WOMAC score (p < 0.0001) and a higher mean SF-12 score (p < 0.0001) than those who were treated non-operatively. Recurrent dislocation and operative treatment increased costs by 300% (£11 456; p < 0.0001) and 40% (£5217; p < 0.0001), respectively. The operative treatment of recurrent dislocation results in significantly better function than non-operative management. Moreover, the increase in costs for operative treatment is modest compared with that of non-operative measures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".