Magnetic resonance imaging of multiple sclerosis: new insights linking pathology to clinical evolution
Bibliographic record
Abstract
Magnetic resonance imaging methods allow observation of pathological changes in vivo. Magnetic resonance-based studies have provided a number of important insights into the spatio-temporal evolution of the pathology of multiple sclerosis in vivo, particularly with respect to the relation between pathology and progression of disability. Magnetic resonance techniques have shown that this pathology is not restricted to the plaques that are evident at autopsy, but also involve the so-called normal-appearing white matter. Nonconventional magnetic resonance imaging strategies such as magnetization transfer imaging and spectroscopic imaging provide measures with higher pathological specificity for myelin and axonal injury. These and other advanced magnetic resonance techniques (such as the measurement of atrophy, lesion relaxation spectra, and lesion dynamics) are affording opportunities to use observations of patients to test biologically specific hypotheses. This should help us to better define new targets for drug therapy and to assess responses to new therapeutic agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".