Early predictors of discharge to home among severely injured geriatric patients: A single-system retrospective cohort study
Bibliographic record
Abstract
Background Injured geriatric patients experience significant functional decline during their hospitalization, limiting their ability to be discharged home which is a valuable outcome among this vulnerable population. We therefore sought to evaluate the clinical characteristics of injured elderly patients managed within our trauma system and identify early predictors for discharge to home. Methods In this single-system retrospective cohort study, we evaluated significantly injured (Injury Severity Score ≥12) geriatric (age ≥65 y) patients admitted from Northern Alberta between 2011 and 2016. The primary outcome was discharge disposition to home. Data was analyzed with descriptive statistics, and univariable and multivariable logistic regression modelling. P values less than 0.05 were considered statistically significant. Results We identified 1548 patients with a median age of 77. Falls accounted for 47% of injuries with median injury severity score of 22; 47% of patients were discharged home with a median hospital length of stay of 8 days. All-cause in-hospital mortality was 19%. On multivariable regression, age, injury severity score, heart rate, systolic blood pressure, and Glasgow Coma Score were independent predictors for discharge home, as well as hospital and intensive care unit length of stay. Conclusion Nearly half of severely injured geriatric trauma patients were discharged home. The identified predictors provide clues to disposition on admission that trauma providers may use to guide in-hospital care planning, disposition planning, and stimulate early goals of care discussions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".