Delayed intracranial hemorrhage of patients with mild traumatic brain injury under antithrombotics on routine repeat CT scan: a systematic review and meta-analysis
Bibliographic record
Abstract
Patients on antithrombotics experiencing mild traumatic brain injury (mTBI) may benefit from a routine repeat CT scan to detect delayed intracranial hemorrhage (dICH). The primary outcome was the incidence of dICH on routine repeat CT scans of mTBI patients on antithrombotics within an intra-hospital observation period of up to 48 hours. The secondary outcomes were potential risk factors, readmissions, neurosurgical interventions, and mortality. A systematic review and a meta-analysis of single proportions were performed according to the PRISMA and PRESS guidelines. The risk of bias was assessed using Newcastle-Ottawa Scale. Eighteen studies with 4613 patients were included. The pooled incidence of dICH was 2% [95% CI 1-2%] with similar rates between different antithrombotic regimens, even in combination. Of the 67 patients with dICH reported (1.45%), eleven required surgery (0.24%), while six died (0.13%). Loss of consciousness was a risk factor of dICH (risk ratio 3.04 [95%CI 0.96; 9.58]). A total of 48 patients were reported for readmission without associated death or surgical intervention. The contribution of this routine repeat CT scan should be questioned due to the low incidence, the limited clinical significance, and the unsubstantiated clinical benefit of early or systematic detection of dICH.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".