Optimal Timing of Post-Alteplase Brain Computed Tomography: Routine Twenty-Four-Hour Versus Extended Forty-Eight-Hour for Detecting Asymptomatic Intracranial Hemorrhage
Bibliographic record
Abstract
Background: Intracranial hemorrhage (ICH) is a major complication of intravenous alteplase for acute ischemic stroke. Asymptomatic ICH (asICH) does not cause immediate neurological decline but may have medicolegal consequences if undetected. In Asian populations, up to 24% of post-alteplase ICH occurs beyond 24 h, suggesting that routine computed tomography (CT) at 24 h may underestimate its incidence. This study evaluated the diagnostic yield of CT at 24 h versus 48 h to determine the optimal timing for ICH detection. Methods: A retrospective cohort study was conducted at Lampang Hospital from March 2017 to December 2024. Patient outcomes were analyzed using an illness-death multistate model with three health states: no ICH, asICH, and symptomatic ICH (sICH). Three transitions were modeled - from no ICH to asICH, from no ICH to sICH, and from asICH to sICH - assuming irreversible progression. The proportions of asICH detected by CT at 24 h versus 48 h were compared using the exact probability test. Results: After exclusions, a total of 555 patients were eligible (54.8% male, mean age 65 years). Final transition outcomes identified 455 patients with no ICH, 51 with asICH, and 49 with sICH. Brain CT at 24 h detected 30.2% of asICH cases, while 48-h imaging detected 96.8%, with a significant improvement (P < 0.001). Conclusions: CT imaging at 48 h post-alteplase improves asICH detection compared with the 24-h scan, reducing missed cases without added radiation or resource burden. Multicenter validation is needed to confirm whether 48 h should replace the current standard.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".