Age is not associated with intracranial haemorrhage in patients with mild traumatic brain injury and oral anticoagulation
Bibliographic record
Abstract
BACKGROUND: Patients admitted to emergency departments with traumatic brain injury (TBI) are commonly being treated with oral anticoagulants. In contrast to patients without anticoagulant medication, no guidelines, scores or recommendations exist for the management of mild traumatic brain injury in these patients. We therefore tested whether age as one of the high risk factors of the Canadian head CT rule is applicable to a patient population on oral anticoagulants. METHODS: This cross-sectional analysis included all patients with mild TBI and concomitant oral anticoagulant therapy admitted to the Emergency Department, Inselspital Bern, Switzerland, from November 2009 to October 2014 (n = 200). Using a logistic regression model, two groups of patients with mild TBI on oral anticoagulant therapy were compared - those with and those without intracranial haemorrhage. RESULTS: There was no significant difference in age between the patient groups with (n = 86) and without (n = 114) intracranial haemorrhage (p = 0.078). In univariate logistic regression, GCS (OR = 0.419 (0.258; 0.680)) and thromboembolic event as reason for anticoagulant therapy (OR = 0.486 (0.257; 0.918)) were significantly associated with intracranial haemorrhage in patients with mild TBI and anticoagulation (all p < 0.05). However, there was no association with age (p = 0.078, OR = 1.024 (0.997; 1.051)), the type of accident or additional medication with acetylsalicylic acid or clopidogrel ((both p > 0.05; 0.552 (0.139; 2.202) and 0.256 (0.029; 2.237), respectively). CONCLUSION: Our study found no association between age and intracranial bleeding. Therefore, until further risk factors are identified, diagnostic imaging with CCT remains necessary for mild TBI patients on oral anticoagulation of all ages, especially those with therapeutic anticoagulation because of thromboembolic events.
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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 | 0.004 |
| 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) | 0.003 | 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".