Faculty Opinions recommendation of Association between pathological and MRI findings in multiple sclerosis.
Bibliographic record
Abstract
The identification of pathological processes that could be targeted by therapeutic interventions is a major goal of research into multiple sclerosis (MS). Pathological assessment is the gold standard for such identification, but has intrinsic limitations owing to the limited availability of autopsy and biopsy tissue. MRI has gained a leading role in the assessment of MS because it allows doctors to obtain an ante mortem picture of the degree of CNS involvement. A number of correlative pathological and MRI studies have helped to define in vivo the pathological substrates of MS in focal lesions and normal-appearing white matter, not only in the brain, but also in the spinal cord. These studies have resulted in the identification of aspects of pathophysiology that were previously neglected, including grey matter involvement and vascular pathology. Despite these important achievements, numerous open questions still need to be addressed to resolve controversies about how the pathology of MS results in fixed neurological disability.Copyright © 2012 Elsevier Ltd. All rights reserved. PMID: 22441196
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.034 |
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".