Polyethylene thickness is a risk factor for wear necessitating insert exchange
Bibliographic record
Abstract
PURPOSE: The aim of this observational study was to investigate the optimal minimal polyethylene (PE) thickness in total knee arthroplasty (TKA) and identify other risk factors associated with revision of the insert due to wear. METHODS: A total of 84 TKA were followed for 11-16 years. All patients received the same prosthesis design (Interax; Howmedica/ Stryker) with halfbearings: separate PE-inserts medially and laterally. Statistical analysis comprised Cox-regression to correct for confounding. RESULTS: Eight knees (9.5%) had been revised due to thinning inserts and an additional patient is scheduled for revision. PE thickness, diagnosis, BMI and weight are risk factors for insert exchange. For each millimetre decrease in PE thickness, the risk of insert exchange increases 3.0 times, which remains after correction for age, gender, weight, diagnosis and femoral-tibial angle. Insert exchange was 4.73 times more likely in OA-patients compared to RA-patients. For every unit increase in BMI and weight the risk for insert exchange increases 1.40 times and 1.14 times, respectively. CONCLUSIONS: In conclusion we therefore advise against the use of thin PE inserts in modular TKA and recommend PE inserts with a minimal 8-mm thickness.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".