Incidence of Groin Pain After Metal-on-Metal Hip Resurfacing
Bibliographic record
Abstract
UNLABELLED: Metal-on-metal hip resurfacing is offered as an alternative to traditional THA for the young and active adult with advanced osteoarthritis. However, patients undergoing hip resurfacing may be predisposed to persistent groin pain due to insufficient head/neck offset, an uncovered acetabular component, or both. We therefore determined the incidence of groin pain after metal-on-metal hip resurfacing, its impact on patient function, and possible risk factors contributing to groin pain. We evaluated 116 patients with a followup of at least 12 months after surgery (mean, 26 months; range, 12-61 months). The mean age was 48.8 years (range, 24.0-66.3 years), with 21 women (18%) and 95 men (82%). All patients were evaluated clinically and radiographically and had a Harris hip score, WOMAC, UCLA Activity Rating Scale, and the RAND-36 General Health measure; they were specifically asked if they experienced groin pain currently or since their surgery. Although all patients had functional improvements postoperatively, 21 of 116 of the patients (18%) reported groin pain; 12 of these (10% of the total) stated the pain limited their activities of daily living and 11 (10%) required medication for pain. Female patients were at greater risk of having groin pain. Of the patients with groin pain, three patients had muscle atrophy with a joint effusion on CT; one of these patients had revision surgery for the pain. Patients with metal-on-metal resurfacing may have a higher incidence of pain than those with conventional THA. LEVEL OF EVIDENCE: Level IV, therapeutic study. See 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".