Association between insulin resistance and post-ischaemic stroke outcome in patients without diabetes: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Insulin resistance is an independent risk factor for atherosclerosis, coronary artery disease and ischaemic stroke. Currently, insulin resistance is not usually included in post-stroke risk stratification. This systematic review and meta-analysis intends to determine if available scientific knowledge supports an association between insulin resistance and post-stroke outcomes in patients without diabetes. Methods and analysis The authors will conduct a literature search in Medline, Embase, Web of Science and Cochrane Central. The review will include studies that assess the association between elevated insulin homeostasis model of insulin resistance (HOMA-IR) and post-stroke outcome (functional outcome and recurrent stroke). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines will be used. The primary outcome will be post-stroke functional outcome (Modified Rankin Scale), and the secondary outcome will be recurrent ischaemic stroke. Comparison of outcome will be made between highest and lowest HOMA-IR range (as defined in each article included in this systematic review). Risk of bias will be assessed qualitatively. Meta-analysis will be performed if sufficient homogeneity exists between studies. Heterogeneity of outcomes will be assessed by I ². Ethics and dissemination No human or animal subjects or samples were/will be used. The results will be published in a peer-reviewed journal, and will be disseminated at local and international neurology conferences. PROSPERO registration number CRD42020173608.
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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 | 0.078 | 0.106 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.006 |
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".