What Patient and Implant Factors Affect Trunnionosis Severity? An Implant Retrieval Analysis of 664 Femoral Stems
Bibliographic record
Abstract
BACKGROUND: Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and thus a clinical concern. Patient and device factors contributing to the occurrence of trunnionosis have been investigated in prior implant retrieval studies but generally with limited sample sizes and a narrow range of models. The purpose of the present investigation was to determine which patient and device factors were associated with corrosion damage on the femoral stem taper across a large collection of different implant models retrieved following revision hip arthroplasty. METHODS: A retrieval study of 664 hip arthroplasty modular stem components was performed. Patient and device information was collected. Trunnions were imaged under digital microscopy and scored for corrosion damage using a scaling system. Damage was related to patient and device factors using regression analyses. RESULTS: Greater duration of implantation (P = .005) and larger head size (P < .001) were associated with an elevated corrosion class. Older age at index surgery (P = .035), stainless steel stem material (P = .022), indication for revision as bone or periprosthetic fracture (P = .017), and infection (P = .018) and certain larger taper geometries were associated with a decreased corrosion class. CONCLUSION: Factors identified as contributing to a higher or lower risk of more severe corrosion are consistent with most prior smaller retrieval studies. Surgeons should be aware of these risk factors when selecting implants for their patients and when diagnosing trunnionosis in symptomatic hip arthroplasty patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".