Elderly Trauma Patients with Rib Fractures Are at Greater Risk of Death and Pneumonia
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to show that elderly patients admitted with rib fractures after blunt trauma have increased mortality. METHODS: Demographic, injury severity, and outcome data on a cohort of consecutive adult trauma admissions with rib fractures to a tertiary care trauma center from April 1, 1993, to March 31, 2000, were extracted from our trauma registry. RESULTS: Among 4,325 blunt trauma admissions, there were 405 (9.4%) patients with rib fractures; 113 were aged > or = 65. Injuries were severe, with Injury Severity Score (ISS) > or = 16 in 54.8% of cases, a mean hospital stay of 26.8 +/- 43.7 days, and 28.6% of patients requiring mechanical ventilation. Mortality (19.5% vs. 9.3%; p < 0.05), presence of comorbidity (61.1% vs. 8.6%; p < 0.0001), and falls (14.6% vs. 0.7%; p < 0.0001) were significantly higher in patients aged > or = 65 despite significantly lower ISS (p = 0.031), higher Glasgow Coma Scale score (p = 0.0003), and higher Revised Trauma Score (p < 0.0001). After adjusting for severity (i.e., ISS and Revised Trauma Score), comorbidity, and multiple rib fractures, patients aged > or = 65 had five times the odds of dying when compared with those < 65 years old. CONCLUSION: Despite lower indices of injury severity, even after taking account of comorbidities, mortality was significantly increased in elderly patients admitted to a trauma center with rib fractures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".