Factors associated with recovery expectations following vehicle collision: A population-based study
Bibliographic record
Abstract
OBJECTIVE: Positive expectations predict better outcomes for a variety of health conditions including recovery from whiplash-associated disorders, but we know little about which individuals have negative expectations, and therefore may be at risk for poor whiplash-associated disorders recovery. METHODS: We assessed expectations for global recovery in a population-based cohort of 6015 individuals with traffic-related whiplash-associated disorders. We used multinomial logistic regression analysis to model factors associated with expecting to recover slowly, or not recover at all, as opposed to expecting to recover quickly. RESULTS: Depressive symptomatology, lower education, lower income, male gender, younger age, being a passenger in the vehicle, history of neck pain, and greater initial pain (greater percentage of body in pain, greater intensity of neck pain and presence of low back and/or headache pain) were associated with poor expectations for recovery. CONCLUSION: A number of demographic, socioeconomic and injury-related factors were associated with expectations for recovery in whiplash-associated disorders. Two of the strongest associated factors were depressive symptomatology and initial neck pain intensity. These results support using a biopsychosocial approach to evaluate expectancies and their influence on important health outcomes.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".