Surgical treatment of femoroacetabular impingement following slipped capital femoral epiphysis
Bibliographic record
Abstract
Objectives The purpose of this study was to evaluate the existing literature from 2005 to 2016 reporting on the efficacy of surgical management of patients with femoroacetabular impingement (FAI) secondary to slipped capital femoral epiphysis (SCFE). Methods The electronic databases MEDLINE, EMBASE, and PubMed were searched and screened in duplicate. Data such as patient demographics, surgical technique, surgical outcomes and complications were retrieved from eligible studies. Results Fifteen eligible level IV studies were included in this review comprising 261 patients (266 hips). Treatment groups included arthroscopic osteochondroplasty, surgical hip dislocation, and traditional open osteotomy. The mean alpha angle corrections were 32.14° (standard deviation (sd) 7.02°), 41.45° (sd 10.5°) and 6.0° (sd 5.21°), for arthroscopy, surgical hip dislocation, and open osteotomy groups, respectively (p < 0.05). Each group demonstrated satisfactory clinical outcomes across their respective scoring systems. Major complication rates were 1.6%, 10.7%, and 6.7%, for arthroscopy, surgical dislocation and osteotomy treatments, respectively. Conclusion In the context of SCFE-related FAI, surgical hip dislocation demonstrated improved correction of the alpha angle, albeit at higher complication and revision rates than both arthroscopic and open osteotomy treatments. Further investigation, including high-quality trials with standardised radiological and clinical outcome measures for young patients, is warranted to clarify treatment approaches and safety. Cite this article: K. O. Oduwole, D. de Sa, J. Kay, F. Findakli, A. Duong, N. Simunovic, Y. Yi-Meng, O. R. Ayeni. Surgical treatment of femoroacetabular impingement following slipped capital femoral epiphysis: A systematic review. Bone Joint Res 2017;6:472–480. DOI: 10.1302/2046-3758.68.BJR-2017-0018.R1.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".