Risk of permanent medical impairment after road traffic crashes: A systematic review
Bibliographic record
Abstract
PURPOSE: To systematically review the risk of permanent disability related to road traffic injuries (RTIs) and to determine the implications for future research regarding permanent impairment following road traffic crashes. METHODS: We conducted this systematic review according to the preferred reporting items for systematic reviews and meta-analysis statement. An extended search of the literature was carried out in 4 major electronic databases for scientific research papers published from January 1980 to February 2020. Two teams include 2 reviewers each, screened independently the titles/abstracts, and after that, reviewed the full text of the included studies. The quality of the studies was assessed using the strengthening the reporting of observational studies in epidemiology (STROBE) checklist. A third reviewer was assessed any discrepancy and all data of included studies were extracted. Finally, the data were systematically analyzed, and the related data were interpreted. RESULTS: Five out of 16 studies were evaluated as high-quality according to the STROBE checklist. Fifteen studies ranked the initial injuries according to the abbreviated injury scale 2005. Five studies reported the total risk of permanent medical impairment following RTIs which varied from 2% to 23% for car occupants and 2.8% to 46% for cyclists. Seven studies reported the risk of permanent medical impairment of the different body regions. Eleven studies stated the most common body region to develop permanent impairment, of which 6 studies demonstrated that injuries of the cervical spine and neck were at the highest risk of becoming permanent injured. CONCLUSION: The finding of this review revealed the necessity of providing a globally validated method to evaluate permanent medical impairment following RTIs across the world. This would facilitate decision-making about traffic injuries and efficient management to reduce the financial and psychological burdens for individuals and communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".