Adverse local tissue reactions in metal-on-polyethylene total hip arthroplasty due to trunnion corrosion
Bibliographic record
Abstract
Adverse reaction to wear and corrosion debris is a cause for concern in total hip arthroplasty (THA). Modular junctions are a potential source of such wear products and are associated with secondary pseudotumour formation. We present a consecutive series of 17 patients treated at our unit for this complication following metal-on-highly cross-linked polyethylene (MoP) THA. We emphasise the risk of misdiagnosis as infection, and present the aggregate laboratory results and pathological findings in this series. The clinical presentation was pain, swelling or instability. Solid, cystic and mixed soft-tissue lesions were noted on imaging and confirmed intra-operatively. Corrosion at the head-neck junction was noted in all cases. No bacteria were isolated on multiple pre- and intra-operative samples yet the mean erythrocyte sedimentation rate was 49 (9 to 100) and C-reactive protein 32 (0.6 to 106) and stromal polymorphonuclear cell counts were noted in nine cases. Adverse soft-tissue reactions can occur in MoP THA owing to corrosion products released from the head-neck junction. The diagnosis should be carefully considered when investigating pain after THA. This may avoid the misdiagnosis of periprosthetic infection with an unidentified organism and mitigate the unnecessary management of these cases with complete single- or two-stage exchange.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".