A paradoxical relationship between hemoglobin A1C and in-hospital mortality in intracerebral hemorrhage patients
Bibliographic record
Abstract
OBJECTIVES: The relationship between prior glycemic status and outcomes in intracerebral hemorrhage (ICH) is not established. We hypothesized that higher hemoglobin (Hb) A1c is associated with worse outcomes in ICH. PATIENTS AND METHODS: Using the GWTG-Stroke registry, data on patients with ICH between April 1, 2003 and September 30, 2015 were harvested. Patients were divided into four ordinal groups based on HbA1c values of <5.7%, 5.7-6.4%, 6.5-8.0% and >8.0%. Outcomes (mortality, modified Rankin Scale (mRS), home discharge and independent ambulatory status) were analyzed for patients overall and separately for patients with or without history of diabetes using multivariable regression models. RESULTS: Among 75,455 patients with ICH (with available HbA1c data), patients with lower HbA1c (<5.7%) had higher rates of in-hospital mortality in the entire cohort (15.5%; 3947/25473); as well as those with history of diabetes (19.0%; 542/2852). Among those without history of diabetes, both lower HbA1c (15.1%; 3405/22621) and higher HbA1c (>8.0%), (15.0%; 205/1364) were associated with higher in-hospital mortality. Lower HbA1c was also associated with higher mRS, less chance of going home, and lower likelihood of having independent ambulatory status in patients with prior history of diabetes. CONCLUSIONS: Among patients with no reported history of diabetes, both very low and very high HbA1c were directly associated with higher in-hospital mortality. Only very low HbA1c was associated with higher mortality in known diabetic patients. 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".