Serum insulin-like growth factor 1 (IGF-1) in multiple sclerosis: relation to cognitive impairment and fatigue
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic demyelinating central nervous system (CNS) disease. Changes in insulin growth factor 1 (IGF-1) input to the brain can affect survival of myelin and CNS cells. The study aims to investigate the relation of serum IGF-1 levels with cognitive impairment and fatigue in MS patients.This study was conducted on 46 MS patients and 46 healthy controls. All participants were subjected to clinical assessment, serum IGF-1 levels, expanded disability status scale (EDSS), modified fatigue impact scale (MFIS), and Montreal cognitive assessment (MoCA) scale.There was no significant difference between patients and controls regarding serum IGF-1 levels (P = 0.19). However, low serum levels of IGF-1 have significantly greater odds for fatigue (P = 0.002) and cognitive impairment (P < 0.001). Also, serum IGF-1 levels have a significant negative correlation with MFIS (r = - 0.701 and P < 0.001) and a significant positive correlation with MoCA scale (r = + 0.84 and P < 0.001).The results, specifically that low levels of serum IGF-1 was associated with cognitive impairment and fatigue in MS, suggest that IGF-I may be involved in the pathogenesis of cognitive deficits and fatigue in MS disease.
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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 | 0.000 | 0.001 |
| 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".