Long-Term Outcomes after Injury in the Elderly
Bibliographic record
Abstract
BACKGROUND: The lasting impact of injury on lifestyle in the elderly remains poorly defined. The purpose of this study was to determine the long-term quality-of-life outcomes in elderly trauma patients. METHODS: The trauma registry at a regional trauma center was used to identify hospital survivors of injury > or = 65 years old discharged from April 1996 to March 1999. The 36-Item Short Form (SF-36) Health Survey was administered to this group by telephone interview and the scores compared with age-adjusted Canadian norms. Comparisons with test were made for continuous data. RESULTS: Complete data collection was achieved in 128 of 171 (75%) study patients. The mean Injury Severity Score was 21, the mean initial Glasgow Coma Scale score was 13, and the mean age was 74. Most (97%) were victims of blunt trauma. Compared with Canadian age-adjusted norms, there was a significant (p < 0.05) decrease in seven of eight SF-36 domains: Physical Functioning, Role-Physical and Role-Emotional (limitations secondary to physical and emotional health), Social Functioning, Mental Health, Vitality, and General Health. Before injury, most (98%) were living independently at home. However, at long-term follow-up (mean, 2.8 years; range, 1.5-4.5 years), only 63% were living independently and 20% still required home care. CONCLUSION: Although the majority of elderly injury survivors achieve independent living, long-term follow-up indicates significant residual disability in quality of life as measured by the SF-36.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".