Optimal Glucose Target After Aneurysmal Subarachnoid Hemorrhage: A Matched Cohort Study
Bibliographic record
Abstract
BACKGROUND: Hyperglycemia has been associated with poor outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH). However, there remains debate as to what optimal glucose targets should be in this patient population. OBJECTIVE: To assess whether we could identify an optimal glucose target for patients with aSAH. METHODS: We performed a post hoc analysis of the "clazosentan to overcome neurological ischemia and infarction occurring after subarachnoid hemorrhage" trial data set. Patients had laboratory results drawn daily for the entirety of their intensive care unit stay. Maximum blood glucose levels were assessed for a relationship with unfavorable outcomes using multiple logistic regression analysis. Maximum blood glucose levels were dichotomized based on the Youden index, which identified a maximum level of <9.2 mmol/L as the optimal cut point for prediction of unfavorable outcomes. Nearest neighbor matching was used to assess the relationship between maintaining glucose levels below the cut point and unfavorable functional outcomes (defined as a modified Rankin score of >2 at 3 mo post-aSAH). The matching was performed after calculation of a propensity score based on identified predictors of outcome and glucose levels. RESULTS: Three hundred eighty-nine patients were included in the matched analysis. Propensity scores were balanced on both the covariates and outcomes of interest. There was a significant average treatment effect (-0.143: 95% confidence interval -0.267 to -0.019) for patients who maintained glucose levels <9.2 mmol/L. CONCLUSION: Maintaining glucose levels below the identified cut point was associated with a decreased risk for unfavorable outcomes in this retrospective matched study.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".