Insulin-Like Growth Factor I Serum Levels Influence Ischemic Stroke Outcome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Insulin-like growth factor I (IGF-I) is neuroprotective in animal models of stroke. We investigated whether serum IGF-I levels in patients with acute ischemic stroke influence stroke severity and outcome. METHODS: Concentrations of IGF-I and IGF binding protein 3 were measured in serum samples obtained within 6 hours after stroke onset from 255 patients who took part in the placebo arm of the United States and Canadian Lubeluzole in Acute Ischemic Stroke Study. Stroke severity was assessed with the National Institutes of Health Stroke Scale. Multivariate analysis was performed to assess the overall shift in modified Rankin Scale score and changes in the National Institutes of Health Stroke Scale score at 3 months. Survival curves were plotted using the Kaplan-Meier method, and the Cox proportional hazard model was used for multivariate analysis to investigate factors influencing survival. RESULTS: After controlling for statistically relevant risk factors, subjects with high IGF-I levels or IGF-I/IGF binding protein 3 ratios had a better neurological and functional outcome at 3 months. Baseline stroke severity was not different between high and low IGF-I groups. In contrast to the low IGF-I group, neurological symptoms gradually improved from Day 3 in the high IGF-I group. CONCLUSIONS: Our results suggest that high serum IGF-I levels just after ischemic stroke onset are associated with neurological recovery and a better functional outcome.
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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 | 0.000 | 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".