Pathology‐guided MR analysis of acute and chronic experimental allergic encephalomyelitis spinal cord lesions at 1.5T
Bibliographic record
Abstract
PURPOSE: To directly correlate spinal cord pathology of guinea pigs with experimental allergic encephalomyelitis (EAE) to the MRI data obtained at 1.5T. MATERIALS AND METHODS: Spinal cords from EAE animals were imaged in vivo with the following MRI sequences: T2-FSE, PD-FSE, fluid-attenuated inversion recovery (FLAIR)-FSE, T2-CSE, T1-CSE, T1-CSE + gadolinium-DTPA (Gd-DTPA), PD-CSE, and short-tau inversion recovery (STIR)-FSE. The spinal cords were removed and the lesions with specific pathological compositions were identified by histological analysis. Regions of interest (ROIs) were drawn on the corresponding MR images, and signal-to-noise ratios (SNRs) were measured for each MR sequence and compared with controls. RESULTS: The receiver operating characteristic (ROC) analysis of STIR-FSE and PD-CSE was able to differentiate tissue that contained cellular infiltrates with a high degree of accuracy. The SNRs of T2-FSE, STIR-FSE, T2-CSE, PD-CSE, and T1-CSE + Gd-DTPA were elevated in lesions that contained cellular infiltrates alone, whereas the SNRs of PD-CSE and T1-CSE + Gd-DTPA were reduced in demyelinated lesions that also contained inflammation. CONCLUSION: The SNR difference between the two lesion groups suggests that the combination of STIR-FSE, PD-CSE, and T1-CSE + Gd-DTPA sequences may be useful for differentiating inflammatory lesions containing demyelination from lesions with inflammation alone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".