Predictors of poor health and functional recovery following road trauma: protocol of a British Columbian inception cohort study
Bibliographic record
Abstract
Introduction Road trauma (RT) is a major public health problem affecting physical and mental health, and may result in prolonged absenteeism from work or study. It is important for healthcare providers to know which RT survivors are at risk of a poor outcome, and policy-makers should know the associated costs. Unfortunately, outcome after RT is poorly understood, especially for RT survivors who are treated and released from an emergency department (ED) without the need for hospital admission. Currently, there is almost no research on risk factors for a poor outcome among RT survivors. This study will use current Canadian data to address these knowledge gaps. Methods and analysis We will follow an inception cohort of 1500 RT survivors (16 years and older) who visited a participating ED within 24 hours of the accident. Baseline interviews determine pre-existing health and functional status, and other potential risk factors for a poor outcome. Follow-up interviews at 2, 4, 6, and 12 months (key stages of recovery) use standardised health-related quality of life tools to determine physical and mental health outcome, functional recovery, and healthcare resource use and lost productivity costs. Ethics and dissemination TheRoad Trauma Outcome Studyis approved by our institutional Research Ethics Board. This study aims to provide healthcare providers with knowledge on how quickly RT survivors recover from their injuries and who may be more likely to have a poor outcome. We anticipate that this information will be used to improve management of all road users following RT. Healthcare resource use and lost productivity costs will be collected to provide a better cost estimate of the effects of RT. This information can be used by policy-makers to make informed decisions on RT prevention programmes.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".