Acetabular Polyethylene Wear and Acetabular Inclination and Femoral Offset
Bibliographic record
Abstract
UNLABELLED: Restoration of femoral offset and acetabular inclination may have an effect on polyethylene (PE) wear in THA. We therefore assessed the effect of femoral offset and acetabular inclination (angle) on acetabular conventional (not highly cross-linked) PE wear in uncemented THA. We prospectively followed 43 uncemented THAs for a minimum of 49 months (mean, 64 months; range, 49-88 months). Radiographs were assessed for femoral offset, acetabular inclination, and conventional PE wear. The mean (+/- standard deviation) linear wear rate in all THAs was 0.14 mm/year (+/- 0.01 mm/year) and the mean volumetric wear rate was 53.1 mm(3)/year (+/- 5.5 mm(3)/year). In THAs with an acetabular angle less than 45 degrees , the mean wear was 0.12 mm/year (+/- 0.01 mm/year) compared with 0.18 mm/year (+/- 0.02 mm/year) in those with a reconstructed acetabular angle greater than 45 degrees . Reproduction of a reconstructed femoral offset to within 5 mm of the native femoral offset was associated with a reduction in conventional PE wear (0.12 mm/year versus 0.16 mm/year). Careful placement of the acetabular component to ensure an acetabular angle less than 45 degrees in the reconstructed hip allows for reduced conventional PE wear. LEVEL OF EVIDENCE: Level II, prospective 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".