Time-to-INR reversal, Predictors of Hematoma Expansion and In-hospital Mortality in Warfarin-related Intracerebral Hemorrhage. (P3.092)
Bibliographic record
Abstract
OBJECTIVE: Evaluate the predictors of hematoma expansion(HE) and in-hospital mortality in warfarin-related intracerebral hemorrhage(wr-ICH), and their relationship to INR reversal. BACKGROUND: Wr-ICH is a devastating disease with case-fatality of 55[percnt]. Therapy entails INR reversal, however whether this improves outcomes is not well established. DESIGN/METHODS: Consecutive cases of ICH admitted between 11/2008-04/2014,(n=287) were reviewed for patients with wr-ICH and INR values >1.4. HE was defined as more than 6ml or 33[percnt] growth between baseline and follow-up CT scans. We compared baseline characteristics and time-to-INR reversal between patients with HE, as well as in-hospital mortality, and those without. RESULTS: In 37 patients with wr-ICH, median baseline ICH volume was 21ml and median time from CT to INR reversal was 471 minutes. Patients with larger ICH volumes had shorter time-to-INR reversal (Spearman’s correlation coefficient: -0.46,p<0.01), higher NIHSS (0.56,p=0.001) and lower Glasgow coma scale(GCS, -0.42,p=0.02). HE and in-hospital mortality occurred in 65[percnt] and 30[percnt] of cases. In comparison to patients without HE, those with HE had overrepresentation of lobar ICH (59[percnt] vs. 11[percnt],p=0.04) and in-hospital mortality (41[percnt] vs. 0[percnt],p=0.06), and underrepresentation of left ventricular hypertrophy (LVH, 30[percnt] vs. 86[percnt],p=0.05). Patients with in-hospital mortality had higher baseline ICH volumes (median: 61 ml vs. 19 ml,p=0.10), lower GCS (median: 11 vs. 14,p=0.08), and less LVH (0[percnt] vs. 62[percnt],p=0.06). The improved outcomes observed with LVH were not explained by medication use, hematoma volume or ICH topography. When analyzing patients with baseline volumes <30ml, in-hospital mortality was associated with higher time-to-INR reversal (median: 826 vs. 535 minutes,p=0.07). CONCLUSIONS: Our results suggest that patients with larger wr-ICH volumes receive more urgent INR reversal, whereas it is those with smaller volumes that are most likely to benefit. LVH may be protective against HE possibly via accompanying cerebral vascular changes that are resistant to hematoma expansive forces. Study Supported by: None
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".