ASSOCIATION OF THE TYPE OF INTRACEREBRAL HEMORRHAGE WITH SERIOUS COMPLICATIONS AND PREDICTIVE FACTORS FOR HEMORRHAGIC TRANSFORMATION AFTER THROMBOLYTIC TREATMENT IN PATIENTS WITH ACUTE ISCHEMIC STROKE
Bibliographic record
Abstract
Background: Accurate classification of postthrombolytic intracerebral hemorrhage (ICH) subtypes is vital for predicting stroke outcomes and managing ICH. Currently, the recommended classification criteria are the European Cooperative Acute Stroke Study III criteria, including two primary categories: hemorrhagic infarction (HI) and parenchymal hematoma (PH). Objectives: The primary objective of this study was to assess the contribution of various ICH subtypes to serious complications, with the secondary aim to identify associated predictors.Methods: The study examined medical records of patients with acute ischemic stroke receiving thrombolysis at Saraburi Hospital from 2014 to 2022. The logit model with the margins command assessed the association of ICH subtypes with serious complications, and multinomial logistic regression identified potential predictors for HI and PH. Results: Among 345 patients, HI-1, HI-2, PH-1 and PH-2 had prevalence rates of 3.2, 7.8, 4.9 and 7.5%, respectively, while 76.5% did not have ICH. PH-2 demonstrated the strongest correlation with inhospital mortality (adjusted risk ratio [RR] 2.83, 95% CI 1.56-5.13), invasive mechanical ventilator requirement (adjusted RR 3.93, 95% CI 2.09-7.39) and hematoma evacuation (adjusted RR 4.58, 95% CI 1.17-17.95) compared with patients of non-ICH. HI demonstrated a significant prolongation of hospitalization. (adjusted RR 3.30, 95% CI 1.53-7.12). Multinomial logistic regression analysis revealed that prior use of antiplatelet drugs, antihypertensive treatment before rt-PA, white blood cell count ≥11,750 cells/mm3 and baseline Alberta stroke program early CT scores ≤7 were independent predictors for PH. The adjusted odds ratios were 3.06 (95% CI, 1.23-7.57), 6.95 (95% CI, 2.62-18.45), 6.01 (95% CI, 2.17-16.65) and 5.01 (95% CI, 2.00-12.60), respectively. Conclusion: The PH-2 subtype was associated with the highest mortality, while our study demonstrated that the HI subtype, previously considered relatively benign with successful early recanalization, showed a significant prolongation of hospitalization compared with that of patients of non-ICH. High-risk patients of ICH require intensive monitoring to reduce complications.
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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 | 0.004 |
| 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) | 0.001 | 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".