The impact of intensity variations in T1-hypointense lesions on clinical correlations in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The correlations between T1-hypointense lesion ('black hole') volume and clinical measures have varied widely across previous studies. The degree of hypointensity in black holes is associated with the severity of tissue damage, but the impact on the correlation with disability is unknown. OBJECTIVES: To determine how variations in the intensity level used for lesion classification can impact clinical correlation, specifically with the Expanded Disability Status Scale (EDSS), and whether using a restricted range can improve correlation. METHODS: A highly automated image analysis procedure was applied to the scans of 24 multiple sclerosis (MS) patients with well-distributed EDSS scores to compute their black hole volumes at nine different levels of intensity relative to the reference intensities sampled in normal-appearing white matter (NAWM) and cerebrospinal fluid (CSF). Two methods of volume computation were used. RESULTS: The black hole volume-EDSS Spearman correlations ranged between 0.49-0.73 (first method) and 0.54-0.74 (second method). The strongest correlations were observed by only including the voxels with maximum intensities at 30-40% of the CSF to NAWM range. CONCLUSIONS: Intensity variations can have a large impact on black hole-EDSS correlation. Restricting the measurement to a subset of the darkest voxels may yield stronger correlations.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".