Preinjury Warfarin Use Among Elderly Patients With Closed Head Injuries in a Trauma Center
Bibliographic record
Abstract
BACKGROUND: This study aimed to determine the impact of warfarin use on the severity of injury among elderly patients presenting with closed head injuries. METHODS: A cohort of patients 55 years of age or older with closed head injuries taken to a tertiary trauma center between April 1993 and March 2001 was retrospectively identified. Patient characteristics, mechanism of injury, type and severity of injury, and hospital survival data were obtained from the trauma registry. Each case then was reviewed for completeness of information, assessment of preinjury warfarin use, and comorbidity. RESULTS: Among the 384 patients presenting with closed head injuries, 35 (9%) were receiving warfarin before their trauma. As compared with nonusers, anticoagulated patients had a higher frequency of isolated head trauma (54% vs. 32%; p = 0.008), more severe head injuries (65.7% vs. 44.1%; p = 0.02), and a higher rate of mortality (40% vs. 21%, p = 0.01). These associations remained evident even after population differences in age, gender, comorbidities, and mechanism of injury were taken into account. Indeed, according to multivariate logistic regression models, warfarin use was associated with a statistically significant risk of death (odds ratio [OR], 2.73; 95% confidence interval [CI], 1.22-6.12), statistically significant odds for more severe head injury (OR, 2.39; 95% CI, 1.10-5.17), and odds for isolated head injury that almost reached statistical significance (OR, 1.79; 95% CI, 0.82-3.90). CONCLUSIONS: Among patients 55 years of age or older who present with closed head injury, the use of warfarin before trauma appears to be associated with a higher frequency of isolated head trauma, more severe head trauma, and a higher likelihood of death. The findings of this retrospective study support the concern about the adverse effects of anticoagulants in cases of head trauma.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".