The influence of femoral offset on health-related quality of life after total hip replacement
Bibliographic record
Abstract
Several factors have been implicated in unsatisfactory results after total hip replacement (THR). We examined whether femoral offset, as measured on digitised post-operative radiographs, was associated with pain after THR. The routine post-operative radiographs of 362 patients (230 women and 132 men, mean age 70.0 years (35.2 to 90.5)) who received primary unilateral THRs of varying designs were measured after calibration. The femoral offset was calculated using the known dimensions of the implants to control for femoral rotation. Femoral offset was categorised into three groups: normal offset (within 5 mm of the height-adjusted femoral offset), low offset and high offset. We determined the associations to the absolute final score and the improvement in the mean Western Ontario and McMaster Universities osteoarthritis index (WOMAC) pain subscale scores at three, six, 12 and 24 months, adjusting for confounding variables. The amount of femoral offset was associated with the mean WOMAC pain subscale score at all points of follow-up, with the low-offset group reporting less WOMAC pain than the normal or high-offset groups (six months: 7.01 (sd 11.69) vs 12.26 (sd 15.10) vs 13.10 (sd 16.20), p = 0.006; 12 months: 6.55 (sd 11.09) vs 9.73 (sd 13.76) vs 13.46 (sd 18.39), p = 0.010; 24 months: 5.84 (sd 10.23) vs 9.60 (sd 14.43) vs 13.12 (sd 17.43), p = 0.004). When adjusting for confounding variables, including age and gender, the greatest improvement was seen in the low-offset group, with the normal-offset group demonstrating more improvement than the high-offset group.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".