Does pelvic tilt change with a peri-acetabular osteotomy?
Bibliographic record
Abstract
ABSTRACT Change in pelvic tilt (PT) during and after peri-acetabular osteotomy (PAO) is important for surgical planning. The aims of this study were to (i) determine how PT varies throughout the course of treatment in patients undergoing PAO, (ii) test what factors influence the change in PT and (iii) assess whether changes in PT influenced achieved correction. This is an retrospective, single-centre, consecutive case series of 111 patients treated with PAO for global (n = 79), posterior (n = 49) or anterior dysplasia (n = 6) (mean age: 27.3 ± 7.7 years; 85% females). PT was determined on supine, anteroposterior pelvic radiographs pre-, intra-, 1 day, 6 weeks and 1 year post-operatively, using the sacro-femoral-pubic (SFP) angle, a validated, surrogate marker of PT. An optimal acetabular correction was based on the lateral centre-edge angle (25°–40°), acetabular index (−5° to 10°) and cross-over ratio (<20%). There was a significant difference across pre- (70.1° ± 4.8°), 1-day (71.7° ± 4.3°; P < 0.001) and early post-operative SFP (70.6° ± 4.7°; P = 0.004). The difference in SPF between pre-operative and 1-year post-operative was −0.5° ± 3.1° (P = 0.043), with 9% of cases having a difference of >5°. The difference in SFP did not correlate with age, sex, body mass index, type of dysplasia or achievement of optimal acetabular correction (P = 0.1–0.9). In the early post-operative period, PT is reduced, leading to a relative appearance of acetabular retroversion, which gradually corrects and is restored by annual follow-up. The degree of change in PT during PAO did not adversely affect fragment orientation. PT does not significantly change in most patients undergoing PAO and therefore does not appear to be a compensatory mechanism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".