593 Quality of life following a road traffic injury: a systematic literature review
Bibliographic record
Abstract
Background Every year, 1.3 million people are killed and up to 50 million are injured in road traffic accidents worldwide. The burden of road traffic injury has shifted from the premature death to a reduced quality of life (QoL) for those injured, therefore it is important to understand the effect of road traffic injury (RTI) on QoL. We aimed to assess and conclude the current knowledge about the relationship between RTI and QoL. Methods A systematic review of the literature on QoL after an RTI, in both adults and paediatric populations, from 3 databases (Pubmed, PsychInfo and SafetyLit) over the last fourteen years was undertaken. The methodological quality of the studies was assessed according to the Newcastle-Ottawa Quality Assessment Scale. Results Nineteen articles were included and assessed for quality. In general, the QoL scores of those injured were similar to population norms at the first assessment, followed by a drop from the first assessment to the second assessment. The majority of the participants reported an increase of QoL from the second assessment to the third assessment but they never reached the population norms at the last follow-up (range 6 weeks to 2 years). Age, gender, socioeconomic status, injury severity, injury type, and PTSD were associated with poorer QoL. Conclusions The available literature regarding the QoL of persons injured in road traffic accidents is heterogeneous in regards to aims and tools used for assessment. Our review confirmed that independent of how QoL was measured, the overall QoL is significantly reduced after an RTI compared to the general population norms. Persons who are older, of female gender, lower socioeconomic status, diagnosed with PTSD, with more severe injuries or injuries to the lower limbs are more vulnerable to loss of QoL following an RTI compared to other patient groups injured in road traffic accidents.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".