Lesion-level correspondence and longitudinal properties of paramagnetic rim and slowly expanding lesions in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Paramagnetic rim lesions (PRLs) and slowly expanding lesions (SELs) have been posited as markers of chronic active lesions (CALs). OBJECTIVE: To assess the lesion-level concordance of PRLs and SELs in MS and to characterize changes in brain tissue integrity in CALs over time. METHODS: MRIs were analyzed from a substudy of AFFINITY [NCT03222973], a phase 2 trial of opicinumab in relapsing MS. Assessments included (1) identification of SELs based on longitudinal MRIs over 72 weeks, and identification of PRLs on susceptibility-weighted imaging (SWI) filtered phase images at week 72; (2) evaluation of subject-level correlation of SEL and PRL counts, volumes, and degree of lesion-level overlap between SELs and PRLs; and (3) characterization of tissue integrity over time in overlapping and non-overlapping SELs and PRLs. RESULTS: In 41 subjects, 119 chronic PRLs and 267 SELs were detected. Of 119 (39.5%) chronic PRLs, 47 co-localized with a SEL; 46/267 (17.2%) SELs co-localized with a PRL. PRLs co-localized with SELs showed expansion and worsening microstructural damage over time. SELs with and without co-localization with PRLs showed ongoing tissue damage. CONCLUSIONS: Chronic MS lesions identified as both PRL and SEL were associated with the most severe accumulation of tissue damage. TRIAL REGISTRATION: AFFINITY [NCT03222973].
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".