An Appraisal of Trauma in the Elderly
Bibliographic record
Abstract
To review the trends of trauma in the elderly experienced at our trauma center compared with other Level I trauma centers. This was a retrospective trauma registry analysis (1996-2003) of 2783 blunt trauma in elderly (BTE) and 4568 adult (BTA) patients in a Level 1 trauma center. Falls and motor vehicular crashes were the most common mechanisms noted in 47 per cent and 31 per cent (84% and 13% in BTE, 25% and 42% in BTA). BTE were sicker, with higher Injury Severity Scores (ISS), lengths of stay, and mortality (5% vs 2%, P value < 0.05). ISS was 5.2-fold higher in nonsurvivors to survivors in BTA and 2.4-fold in BTE. Elevation in ISS resulted in higher linear increase in mortality in BTE (vs BTA) at any ISS level. Mortality in patients with ISS > or = 25 was 43.5 per cent vs 23.8 per cent. ISS > or = 50 had 31 per cent adult survivors but no elderly survivors. Among isolated injuries, head trauma in the elderly carried the highest mortality, at 12 per cent (19% in patients with an Abbreviated Injury Score > or = 3). Abdominal injuries were the most lethal (18.3% and 41.2% in patients with an Abbreviated Injury Score > or = 3) in multiple trauma victims (41% vs 18% in isolated trauma). There was 4.4-fold increased mortality in the presence of thoracic trauma. Combined head, chest, and abdominal trauma carried the worst prognosis. Thirty-four per cent of BTE and 88 per cent of BTA patients were discharged home. Elderly patients need more aggressive therapy, as they are sicker with higher mortality.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".