Mortality among older adults after a traumatic brain injury: A meta-analysis
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To examine mortality rates among older adults (≥60 years) post-traumatic brain injury (TBI). RESEARCH DESIGN: Systematic review and meta-analysis. METHODS AND PROCEDURES: Using multiple databases, a literature search was conducted for articles on mortality after TBI published up to July 2011. Information on patient characteristics (age, Glasgow Coma Scale (GCS), injury aetiology, etc.), mortality rates, time to death and study design was extracted and pooled. MAIN OUTCOMES AND RESULTS: Twenty-four studies had an overall mortality rate of 38.3% (CI 27.1-50.9%). The odds of mortality for those over 75 years compared to those of 65-74 years was 1.734 (CI = 1.311-2.292; p < 0.0001). Pooled mortality rates for mild (GCS 13-15), moderate (GCS 9-12) and severe (GCS 3-8) head injuries were 12.3% (CI = 6.1-23.3%), 34.3% (CI = 19.5-53.0%) and 65.3% (CI = 53.1-75.9), respectively. Odds ratios comparing severe to mild and moderate to mild head injuries were 12.69 (CI = 5.29-30.45; p < 0.0001) and 5.31 (CI = 3.41-8.29; p < 0.0001), respectively. There was no significant difference in the odds of death between severe and moderate injuries (p = 0.116). CONCLUSIONS: These mortality rates associated with moderate and severe injuries may be attributed to complications, chronic disease prevalence, conservative management techniques or the consequences of biological ageing.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".