Early Predictors of Clinical and<scp>MRI</scp>Outcomes Using<scp>Least Absolute Shrinkage and Selection Operator (LASSO)</scp>in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to identify predictors in common between different clinical and magnetic resonance imaging (MRI) outcomes in multiple sclerosis (MS) by comparing predictive models. METHODS: We analyzed 704 patients from our center seen at MS onset, measuring 37 baseline demographic, clinical, treatment, and MRI predictors, and 10-year outcomes. Our primary aim was identifying predictors in common among clinical outcomes: aggressive MS, benign MS, and secondary-progressive (SP)MS. We also investigated MRI outcomes: T2 lesion volume (T2LV) and brain parenchymal fraction (BPF). The performance of the full 37-predictor model was compared with a least absolute shrinkage and selection operator (LASSO)-selected model of predictors in common between each outcome by the area under the receiver operating characteristic curves (AUCs). RESULTS: The full 37-predictor model was highly predictive of clinical outcomes: in-sample AUC was 0.91 for aggressive MS, 0.81 for benign MS, and 0.81 for SPMS. After variable selection, 10 LASSO-selected predictors were in common between each clinical outcome: age, Expanded Disability Status Scale, pyramidal, cerebellar, sensory and bowel/bladder signs, timed 25-foot walk ≥6 seconds, poor attack recovery, no sensory attacks, and time-to-treatment. This reduced model had comparable cross-validation AUC as the full 37-predictor model: 0.84 versus 0.81 for aggressive MS, 0.75 versus 0.73 for benign MS, and 0.76 versus 0.75 for SPMS, respectively. In contrast, 10-year MRI outcomes were more strongly influenced by initial T2LV and BPF than clinical outcomes. INTERPRETATION: Early prognostication of MS is possible using LASSO modeling to identify a limited set of accessible clinical features. These predictive models can be clinically usable in treatment decision making once implemented into web-based calculators. ANN NEUROL 2022;92:87-96.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".