Comparison of inversion recovery gradient echo with inversion recovery fast spin echo techniques for magnetic resonance imaging detection of navicular bone marrow lesions in horses
Bibliographic record
Abstract
OBJECTIVE: To compare navicular bone marrow lesion (BML) conspicuity in the feet of horses as determined via 2 fat-suppressed MRI techniques, including standard short tau inversion recovery (STIR) and inversion recovery gradient echo (IRGE). SAMPLE: Feet (n = 150) of horses with lameness referable to the distal portion of the digit. PROCEDURES: STIR and IRGE sequences were obtained prospectively in all feet with a standing low-field equine MRI system. Presence of a BML was ascertained by identification of a characteristic combination of marrow alterations in T1-weighted, T2*-weighted, T2-weighted, and STIR images. Signal-to-noise and contrast-to-noise ratios were calculated on STIR and IRGE sequences in 56 feet with a navicular BML. RESULTS: Signal-to-noise and contrast-to-noise ratios of both sequences correlated linearly (r = 0.87 and r = 0.92, respectively) but were significantly higher for STIR images (mean ± SD, 22.6 ± 12.7 and 12.4 ± 11.4, respectively), compared with IRGE images (13.7 ± 8.0 and 5.9 ± 7.2, respectively). CONCLUSIONS AND CLINICAL RELEVANCE: Results suggested that the IRGE sequence revealed BMLs significantly less conspicuously, compared with the standard STIR sequence. The 2 techniques cannot be used interchangeably, and IRGE is therefore not recommended as the sole fat-suppressed sequence for routine equine standing MRI protocols.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".