Abstract WP344: Lower Hemoglobin A1c is Associated With Higher In-Hospital Mortality in Intracerebral Hemorrhage
Bibliographic record
Abstract
Introduction: Relationship of prior glycemic status to outcomes in intracerebral hemorrhage (ICH) is not established. Hypothesis: Higher hemoglobin (Hb) A1c is associated with worse outcomes in ICH. Methods: Data harvested from GWTG-Stroke registry on patients with ICH between April 1, 2003 and September 30, 2015. Four ordinal groups made based on HbA1c values of <5.7%, 5.7-6.4%, 6.5-8.0% and >8.0%. Outcomes analyzed overall and separately for patients with or without history of diabetes using unadjusted and adjusted multivariable regression models. Results: Among 75,455 patients with ICH from 1,336 sites, the prevalence of diabetes was 36.2% and prediabetes (no prior history of diabetes and HbA1c between 5.7-6.4%) was 41.9%. An increasing trend in the median BMI values was noted across the groups, with worse HbA1c associated with higher BMI. Lower HbA1c was associated with higher in-hospital mortality (3947/25473 {15.5%} overall, 542/2852 {19.0%} in patients with history of diabetes, and 3405/22621{15.1%} in patients without history of diabetes) (Table), higher mRS, less chance of going home, and lower likelihood of having independent ambulatory status, also seen in unadjusted and adjusted outcomes (Figure). Only in patients with no prior diabetes history, both higher HbA1c and lower HBA1c were associated with higher in-hospital mortality. Conclusions: In this large cohort of ICH patients, contrary to our original hypothesis, lower HbA1c (rather than higher HbA1c) was associated with higher in-hospital mortality. Further studies are needed to better define the relationship between HbA1c and outcomes, for it may have important implications for care of ICH patients.
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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 | 0.001 | 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) | 0.006 | 0.001 |
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.
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".