Abstract P451: Clinical Characteristics of Coagulopathic Intracranial Hemorrhages
Bibliographic record
Abstract
Introduction: Acute non-traumatic intracerebral hemorrhage (ICH) is the second-most common subtype of stroke and has a 40% mortality rate at one month. Non-iatrogenic coagulopathies comprise less than 20% of ICH and few studies have investigated characteristics of these groups. We compared baseline characteristics of individuals with ICH with and without non-iatrogenic coagulopathy. Methods: A retrospective study of 392 consecutive patients with ICHs between 2017 and 2019 were analyzed by the absence of coagulopathy, presence of anticoagulation and non-iatrogenic coagulopathy. Non-iatrogenic coagulopathy was defined as a prior documented coagulopathy (such as liver/kidney disease or cancer), platelets <100K, INR >2.0 or aPTT >60 seconds. Location(s) and size of abnormalities on prior imaging and presenting ICH were recorded. A two-tailed type 1 Student’s t -test (continuous variables), chi-squared test (categorical variables), Bonferroni corrections and Cramer’s V were used for statistical analyses. Preliminary Results: Of 392 patients with ICH, 28 (7.1%) were anti-coagulated and 29 (7.4%) had non-iatrogenic coagulopathy. There were lower/comparable rates of hypertension (83%, p=0.02 and p=0.25) and higher rates of alcohol use (28%, p=0.01 and p<0.01) and chronic kidney disease (38%, p<0.01 and p<0.01) in the non-iatrogenic coagulopathy compared to non-coagulopathic and anticoagulated groups, respectively. Highest baseline blood pressure was lower in the non-iatrogenic coagulopathy (systolic p=0.09, diastolic p=0.04) and anticoagulated groups (systolic p=0.02, diastolic p=0.22) compared to non-coagulopathic ICH. Conclusion: Our preliminary findings suggest that individuals with non-iatrogenic coagulopathy are as common as anticoagulated patients in our ICH cohort and have distinct clinical characteristics. Future directions, such as neuroimaging anaysis and assessment of functional outcomes, will determine if there are risk factors that would warrant distinct treatments beyond standard supportive management.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".