EFFECT OF ACQUISITION TIME AND CHEMICAL FAT SUPPRESSION ON MENINGEAL ENHANCEMENT ON MR IMAGING IN DOGS
Bibliographic record
Abstract
Our purpose was to characterize meningeal gadolinium enhancement on magnetic resonance (MR) imaging in dogs with inflammatory and neoplastic diseases, and to assess interobserver variability and the impact of delayed acquisition and chemical fat saturation on its conspicuity. Transverse T1-weighted FLAIR images were acquired prior to, and immediately following gadolinium injection (T0), and at 5 (T5) and 15-20 min delay (TD), with and without fat suppression, in 155 consecutive dogs imaged for suspected brain disease. The agreement on meningeal enhancement was globally substantial (kappa = 0.61) and the likelihood of obtaining a definite diagnosis was significantly increased with the use of fat suppression (P < or = 0.004). Meningeal enhancement was judged definitively present by consensus in 46 of 155 (30%) dogs. Of these, meningeal enhancement was characterized qualitatively and quantitatively in 30 dogs with a clinical diagnosis (18 inflammatory, 11 neoplastic, 1 infarct), and image sequences were compared. Meningeal enhancement was more often diffuse and leptomeningeal in animals with inflammation versus neoplasia (50% vs. 42%, and 69% vs. 48%, respectively), but significant associations were not found. Meningeal thickness and contrast ratio were higher with neoplasia (P < or = 0.02), but results did not vary significantly between series for either group. Yet, images with fat suppression were most useful 50% of the time for definite diagnosis and/or characterization of meningeal enhancement. While delayed image acquisition following gadolinium injection does not improve characterization of meningeal enhancement in dogs, fat suppression is beneficial qualitatively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".