Long term health outcomes after injury in working age adults: a systematic review: Table 1
Bibliographic record
Abstract
BACKGROUND: Estimating the contribution of non-fatal injury outcomes remains a considerable challenge and is one of the most difficult components of burden of disease analysis. The aim of this systematic review was to quantify the effect of being injured compared with not being injured on morbidity and health service use (HSU) in working age adults. METHODS: Studies were selected that were population based, had long term health outcomes measured, included a non-injured comparison group, and related to working age adults. Meta-analysis was not attempted because of the heterogeneity between studies. RESULTS: Nine studies met the inclusion criteria. In general, studies found an overall positive association between injury and increased HSU, exceeding that of the general population, which in some studies persisted for up to 50 years after injury. Disease outcome studies after injury were less consistent, with null findings reported. CONCLUSION: Because of the limited injury types studied and heterogeneity between study outcome measures and follow up, there is insufficient published evidence on which to calculate population estimates of long term morbidity, where injury is a component cause. However, the review does suggest injured people have an increased risk of long term HSU that is not accounted for in current methods of quantifying injury burden.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".