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Record W1989910063 · doi:10.1159/000047757

Effect of Acute Glycaemic Index on Clinical Outcome after Acute Stroke

2002· article· en· W1989910063 on OpenAlexaff
Ajay Bhalla, Suki Sankaralingam, Kate Tilling, R. Swaminathan, Charles Wolfe, Anthony Rudd

Bibliographic record

VenueCerebrovascular Diseases · 2002
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineStroke (engine)Internal medicineAcute strokeOdds ratioBarthel indexPhysical therapyActivities of daily livingTissue plasminogen activator

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.015
GPT teacher head0.309
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2002
Admission routes1
Has abstractyes

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