The use of slice encoding for metal artifact correction (SEMAC) sequencing improves the diagnostic evaluation of graft integrity following anterior cruciate ligament reconstruction
Bibliographic record
Abstract
OBJECTIVES: To determine whether magnetic resonance imaging (MRI) with metal artifact reduction sequencing is superior to conventional knee MRI in the evaluation of an injured anterior cruciate ligament (ACL) graft, where visualisation on conventional MRI can be limited by the metal artifact from fixation devices. METHODS: Eighteen patients underwent conventional MRI sequence (proton density fat saturated [PDFS]) and two types of metal artifact reduction sequencing MRI (WARP, slice encoding for metal artifact correction (SEMAC); Siemens) following a secondary injury to their ACL reconstructed knee. Six raters with experience in knee MRI evaluation reviewed sagittal PDFS, WARP, and SEMAC sequences, providing semi-quantitative grades for visualisation and diagnostic confidence assessing the ACL, posterior cruciate ligament , menisci, tibial and femoral tunnel margins, and articular cartilage. Intra-class correlation coefficients for inter-rater reliability were evaluated. The 6-rater mean scores for the visualisation and diagnostic confidence derived from each sequence were compared using the Friedman test for multiple paired samples. RESULTS: No statistically significant difference in the ACL visualisation among the sequences was found (p = 0.193). Further, a subgroup analysis was performed in cases evaluated as "moderately blurry" or "indistinct ACL visualisation" on PDFS (58% of cases). SEMAC significantly improved diagnostic confidence in ACL visualisation (p = 0.041) and ACL graft rupture (p = 0.044) compared to PDFS. There was no statistically significant difference in the inter-observer reliability between sequences. The WARP sequence added 2.84 ± 0.69 min, while SEMAC added 2.95 ± 0.40 min to the standard knee MRI scan time. CONCLUSION: use of the SEMAC metal reduction sequence significantly improved diagnostic accuracy and confidence in the detection of ACL graft rupture in cases where the ACL was moderately blurry or indistinct on the PDFS sequence. This sequence should be considered as an adjunct to conventional PDFS in cases where graft visualisation is limited by the metal artifact from fixation devices. LEVEL OF EVIDENCE: III.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".