Long-term mortality following injury in working-age adults: a systematic review
Bibliographic record
Abstract
Priorities for prevention activities and planning for services depend on comprehensive knowledge of the distribution of the injury-related burden in the community. The aim of this systematic review was to quantify the effect of being injured, compared with not being injured, on long-term mortality in working age adults. Cohort studies were selected that were population-based, measured mortality post-discharge from inpatient treatment, included a non-injured comparison group and related to working-age adults. Data synthesis was in tabular and text form with a meta-analysis not being possible because of the heterogeneity between studies. Eleven studies met the inclusion criteria. All studies found an overall positive association between injury and increased mortality. While the greatest excess mortality was evident during the initial period post-injury, increased mortality was shown in some studies to persist for up to 40 years after injury. Due to the limited number of injury types studied and heterogeneity between studies, there is insufficient published evidence on which to calculate population estimates of long-term mortality, where injury is a component cause. The review does suggest there is considerable excess mortality following injury that is not accounted for in current methods of quantifying injury burden, and is not used to assess quality and effectiveness of trauma care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".