Identification of Normal and Injured Anterolateral Ligaments of the Knee: A Systematic Review of Magnetic Resonance Imaging Studies
Bibliographic record
Abstract
PURPOSE: To identify the normal and injured magnetic resonance imaging appearance of the anterolateral ligament (ALL). METHODS: A systematic review was performed using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed and Cochrane Library electronic databases were used to search for studies that reported the imaging outcomes of the appearance of the ALL. Two authors performed the searches in duplicate up to April 30, 2018, and interobserver agreement was calculated. The methodologic quality of included articles was assessed using an adaptation of the Arrivé methodologic quality scale for clinical studies of radiologic examinations. RESULTS: From the original 270 records, a total of 24 studies (κ = 0.94) comprising 2,427 knees in 2,388 patients (mean age, 33.3 years; 66% male patients; 63% with anterior cruciate ligament [ACL] injury) were included. The ALL appeared in 51% to 100% of all assessed knees (71%-100% in ACL-injured knees and 64%-97% in uninjured knees) and was injured in 11% to 79% of ACL-injured knees. Reliability rates varied considerably (0.04-1.0 for intraobserver and 0.143-1.0 for interobserver agreement), and the entire portion of the ligament was often not seen. The tibial insertion was seen in 21% to 96% of cases, followed by the meniscal (range, 0%-100%) and femoral (range, 0%-90%) insertions. The mean methodologic quality score was 5.1 ± 1.8 out of a possible score of 9. CONCLUSIONS: High variability was found in the identification of normal and injured ALL definition methods and the respective magnetic resonance imaging findings. Reliability rates varied considerably, and the entire portion of the ligament was often not seen. LEVEL OF EVIDENCE: Level IV, systematic review of Level II to IV studies.
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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 | 0.020 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".