Effect of Acute Glycaemic Index on Clinical Outcome after Acute Stroke
Bibliographic record
Abstract
Studies have shown that hyperglycaemia acutely after stroke independently predicts poorer survival and independence. Whether the change in glycaemic index in the acute phase of stroke has any effect on stroke outcome is unclear. Glycated serum proteins (GSP) reflect blood glucose concentration during the preceding 2 weeks. The aim of this study is to measure the association between the change in GSP % in the first 2 weeks after stroke and outcome. 167 patients were included. 117 (70%) patients were alive at 3 months. Admission glucose was higher in dead patients (7.8 mmol/l) compared to survivors (6.6 mmol/l) (p < 0.01). GSP at day 14 was higher in non survivors (21.8%) compared with survivors (19.1%) (p < 0.0001) as was the change in GSP (2.0 %) in non survivors compared with survivors (0.1%) (p < 0.0001). After adjusting for case mix, the change in GSP % was significantly associated with stroke mortality (p = 0.04). The odds ratio for death at 3 months after stroke associated with every 1% increase in change between GSP day 14 and GSP day 0, was 1.28 (95% CI: 1.1-1.62). Increases in glycaemic index as determined by GSP % are associated with excess in stroke mortality after adjusting for case mix. Intervention trials are required to test the hypothesis that improving glycaemic index after acute stroke improves 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".