Comorbidity and age are both independent predictors of length of hospitalization in trauma patients.
Bibliographic record
Abstract
BACKGROUND: Length of hospitalization is a good indicator of resource utilization. Older patients are increasingly suffering trauma, and comorbid medical conditions are also increasing. Our objective was to determine the separate and combined effect of these 2 factors on length of hospital stay for trauma patients in a tertiary trauma centre. METHODS: All 994 consecutive trauma patients surviving to hospital discharge between Apr. 1, 2000, and Mar. 31, 2001, were identified. Patient characteristics, injury severity and length of hospitalization were obtained from the hospital trauma registry. Each medical record was then reviewed for completeness of information and assessment of comorbid conditions. A multivariate linear regression model was developed to predict logarithmic length of stay from age and presence of a cormorbid condition while adjusting for the Injury Severity Score (ISS). RESULTS: The mean age of the patients was 49.7 (range from 14-100) years and median ISS was 9 (range from 1-50). At least 1 comorbid condition was present in 321 (32%) patients. Mean length of hospital stay was 15.3 days. The proportion of patients with a comorbid condition increased steadily with age, from 8.7% before the age of 55 years to 92% at 85 or more years of age (p < 0.001). According to the multivariate model, the presence of comorbidity, age and ISS were all independent predictors of hospital stay (p < 0.001). When applied to patients with the mean ISS value of 9, the model showed an increase in length of hospitalization for patients with a comorbid condition over those without; (3.6 v. 13.1 d for patients < 55 and > or = 85 yr respectively). Length of hospital stay increased particularly with neurologic and pulmonary problems. CONCLUSIONS: Comorbidity and age were both independently significant predictors of length of hospitalization over and beyond that which is expected based on the severity of the injuries. With an aging population, this phenomenon should severely affect resource utilization in trauma centres in the near future. Researchers should take account of both age and comorbidity in order to compare trauma populations.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".