A Survey on the Prevalence of Pseudotumors with Metal-on-Metal Hip Resurfacing in Canadian Academic Centers
Bibliographic record
Abstract
BACKGROUND: Metal-on-metal bearings for hip arthroplasty have been in clinical use for over twenty years with excellent clinical results reported worldwide. A small percentage (<1%) of patients have developed an inflammatory response, and a more severe inflammatory response termed pseudotumor has been more recently reported. The primary purpose of this study was to determine the prevalence of pseudotumor following metal-on-metal hip resurfacing in Canadian academic centers. METHODS: Nine of the fourteen centers that perform metal-on-metal hip resurfacings were surveyed. The number of metal-on-metal hip resurfacing arthroplasties performed at each center was determined, as was the number of patients who presented with a pseudotumor and subsequently required revision hip surgery between 2002 and December 2008. RESULTS: At the nine centers, 3432 hip resurfacing arthroplasties were performed; 76.9% of the patients were male, the mean age was 51.2 years (range, sixteen to eighty-three years), and the mean body-mass index was 28.1 (range, 17 to 55). The mean length of follow-up was 3.4 years (range, two to nine years). A pseudotumor developed after four of the 3432 arthroplasties, for a prevalence of 0.10%. CONCLUSIONS: Although pseudotumors remain a concern after metal-on-metal hip resurfacing, the prevalence at short to midterm follow-up is very low in this multicenter survey. Continued close monitoring is required to determine what clinical factors are involved with the uncommon pseudotumor formation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".