Impact of immunoablation and autologous hematopoietic stem cell transplantation on gray and white matter atrophy in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Immunoablation and autologous hematopoietic stem cell transplantation (IA/aHSCT) halts relapses, white matter (WM) lesion formation, and pathological whole-brain (WB) atrophy in multiple sclerosis (MS) patients. Whether the latter was due to effects on gray matter (GM) or WM warranted further exploration. OBJECTIVE: To model GM and WM volume changes after IA/aHSCT to further understand the effects seen on WB atrophy. METHODS: GM and WM volume changes were calculated from serial baseline and follow-up magnetic resonance imaging (MRI) ranging from 1.5 to 10.5 years in 19 MS patients treated with IA/aHSCT. A mixed-effects model with two predictors (total busulfan dose and baseline T1-weighted WM lesion volume "T1LV") characterized the time-courses after IA/aHSCT. RESULTS: Accelerated short-term atrophy of 2.1% and 3.2% occurred in GM and WM, respectively, on average. Both busulfan dose and T1LV were significant predictors of WM atrophy, whereas only busulfan was a significant predictor of GM atrophy. Compared to baseline, a significant reduction in GM atrophy, not WM atrophy, was found. The average rates of long-term GM and WM atrophy were -0.18%/year (standard error (SE): 0.083) and -0.07%/year (SE: 0.14), respectively. CONCLUSION: Chemotherapy-related toxicity affected both GM and WM. WM was further affected by focal T1-weighted lesion-related pathologies. Long-term rates of GM and WM atrophy were comparable to those of normal-aging.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".