Preoperative physical examination and imaging of femoroacetabular impingement prior to hip arthroscopy—a systematic review
Bibliographic record
Abstract
The purpose of this systematic review is to report current preoperative assessment for femoroacetabular impingement (FAI) including physical examination and imaging modalities prior to hip arthroscopy, and report current imaging measures used in the diagnosis of FAI. The electronic databases MEDLINE, EMBASE and PubMed were searched and screened in duplicate for relevant studies. Data regarding patient demographics, non-operative treatment, preoperative assessment including physical examination and imaging prior to hip arthroscopy were abstracted. Study quality was assessed in duplicate using the Methodological Index for Non-Randomized Studies criteria. Sixty-eight studies of fair quality evidence that involved a total of 5125 patients (5400 hips) were included. In total, 56% of all patients were male and mean age was 36 years (SD ± 10.0). Within physical examination, FADIR impingement testing was reported in 57% of patients. All included studies reported plain radiographic imaging as a component of preoperative assessment with anterior–posterior pelvis view being the most commonly reported view, followed by the cross-table lateral and Dunn views. Magnetic resonance imaging was obtained for 52% of included patients and computed tomography for 26% of patients. The most commonly reported measure within imaging for the diagnosis of cam type impingement was alpha angle (66%), whereas for pincer type impingement, the cross-over sign (48%) was most reported. Preoperative assessment is underreported in the FAI literature. Improved reporting is warranted to develop a more consistent and validated diagnostic algorithm for FAI to enhance patient selection. Level of evidence: Level IV, Systematic Review of Level I–IV Studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".