Clinically significant traumatic intracranial hemorrhage following minor head trauma in older adults: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this study was to determine the incidence of clinically significant traumatic intracranial hemorrhage (T-ICH) following minor head trauma in older adults. Secondary objective was to investigate the impact of anticoagulant and antiplatelet therapies on T-ICH incidence. METHODS: This retrospective cohort study extracted data from electronic patient records. The cohort consisted of patients presenting after a fall and/or head injury and presented to one of five ED between 1st March 2010 and 31st July 2017. Inclusion criteria were age ≥ 65 years old and a minor head trauma defined as an impact to the head without fulfilling criteria for traumatic brain injury. RESULTS: From the 1,000 electronic medical records evaluated, 311 cases were included. The mean age was 80.1 (SD 7.9) years. One hundred and eighty-nine (189) patients (60.8%) were on an anticoagulant (n = 69), antiplatelet (n = 130) or both (n = 16). Twenty patients (6.4%) developed a clinically significant T-ICH. Anticoagulation and/or antiplatelets therapies were not associated with an increased risk of clinically significant T-ICH in this cohort (Odds ratio (OR) 2.7, 95% CI 0.9-8.3). CONCLUSIONS: In this cohort of older adults presenting to the ED following minor head trauma, the incidence of clinically significant T-ICH was 6.4%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".