An Evaluation of Age-Based Differences in the Demographic Features and Clinical Outcomes of Trauma Rehabilitation Patients
Bibliographic record
Abstract
OBJECTIVE: The aims of the study were to describe potential age-related differences in injury type and mechanism, comorbidities, and physical medicine and rehabilitation-relevant complications in patients admitted after major trauma and to examine whether functional outcomes vary by age group after traumatic injury. DESIGN: This is a subanalysis of a pre-post study. Individuals admitted to a level 1 trauma center who sustained major trauma were divided into three age groups (young, middle age, and elderly). The demographic, acute care, and rehabilitation factors for these patients were then compared across the three age groups. RESULTS: Based on an age distribution plot, the age categories were defined as follows: young, 18-39 yrs (n = 120); middle age, 40-64 yrs (n = 124); and elderly, 65 yrs or older (n = 85). Patients 65 yrs or older demonstrated a greater frequency of comorbidities (P < 0.001) and complications (P < 0.001). For individuals admitted to inpatient rehabilitation, admission and discharge functional independence measure scores were lower for the elderly individuals, but functional independence measure change was not significantly different between groups. CONCLUSIONS: Although the elderly trauma patient demonstrates important differences from the younger one, capacity for improvement with rehabilitation seems similar.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".