Impact of varying levels of hyperglycemia on clinicoradiographic outcomes after endovascular reperfusion treatment
Bibliographic record
Abstract
Abstract We evaluated the effects of admission hyperglycemia with different cut-off levels on 3-month outcomes, infarct growth, and hemorrhagic transformation in acute stroke patients with large artery occlusion of anterior circulation who received endovascular treatment (EVT). Between January 2011 and May 2016, patients that underwent EVT with pre-procedural and post-procedural diffusion-weighted imaging were identified from a multicenter registry. Normoglycemia was defined as a glucose level ≤ 110 mg/dL, moderate hyperglycemia as >110 and ≤170 mg/dL, and overt hyperglycemia as >170 mg/dL. Its effects on poor outcomes (3-month modified Rankin Scale score 3–6), infarct growth, and parenchymal hematoma type 2 were analyzed. Of 720 patients encountered, 341 patients were eligible. There was a statistically significant difference in glycated hemoglobin levels between the normoglycemia/moderate hyperglycemia and overt hyperglycemia groups (p < 0.001). Moderate hyperglycemia (odds ratio 2.37 [95% confidence interval 1.26–4.45], p = 0.007) and overt hyperglycemia (2.84 [1.19–6.81], p = 0.019) were associated with poor outcomes. Post-procedural infarct volumes were significantly greater in hyperglycemic patients (padjusted = 0.003). Only overt hyperglycemia (9.28 [1.66–51.88], p = 0.011) was associated with parenchymal hematoma type 2. Overall hyperglycemia was associated with poor outcomes and infarct growth, whereas overt hyperglycemia was associated with parenchymal hematoma type 2.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".