Incidence of intracranial bleeding in anticoagulated patients with minor head injury: a systematic review and meta‐analysis of prospective studies
Bibliographic record
Abstract
Guidelines advise performing a computed tomography head scan for all anticoagulated head injured patients, but the risk of intracranial haemorrhage (ICH) after a minor head injury is unclear. We conducted a systematic review and meta-analysis to determine the incidence of ICH in anticoagulated patients presenting with a minor head injury and a Glasgow Coma Score (GCS) of 15. We followed Meta-Analyses and Systematic Reviews of Observational Studies guidelines. We included all prospective studies recruiting consecutive anticoagulated emergency patients presenting with a head injury. Anticoagulation included vitamin-K antagonists (warfarin, fluindione), direct oral anticoagulants (apixaban, rivaroxaban, dabigatran and edoxaban) and low molecular weight heparin. A total of five studies (including 4080 anticoagulated patients with a GCS of 15) were included in the analysis. The majority of patients took vitamin K antagonists (98·3%). There was significant heterogeneity between studies with regards to mechanism of injury and methods. The random effects pooled incidence of ICH was 8·9% (95% confidence interval 5·0-13·8%). In conclusion, around 9% of patients on vitamin K antagonists with a minor head injury develop ICH. There is little data on the risk of traumatic intracranial bleeding in patients who have a GSC 15 post-head injury and are prescribed a direct oral anticoagulant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".