Risk Factors for Persistent Problems Following Whiplash Injury: Results of a Systematic Review and Meta-analysis
Bibliographic record
Abstract
STUDY DESIGN: Systematic review and meta-analysis. BACKGROUND: Whiplash-associated disorder (WAD) is the most common reported injury following motor vehicle accident. Evidence for prognosis and intervention are difficult to interpret due to differences in inception times, outcomes used, and sample heterogeneity. METHODS: An extensive literature search was conducted to identify published studies of prognosis following whiplash. Rigorous inclusion criteria were applied to allow for meaningful results to be drawn. Data were extracted, transformed where necessary, and pooled to allow estimation of the odds ratio for any factor with at least 3 data points in the literature. RESULTS: From 11 cohorts (n = 3193), 25 factors were identified with at least 3 data points in the existing literature. Of these, 9 were found to be significant predictors based on the odds ratio and confidence limits: no postsecondary education, female gender, history of previous neck pain,baseline neck pain intensity greater than 55/100, presence of neck pain at baseline, presence of headache at baseline, catastrophizing, WAD grade 2 or 3, and no seat belt in use at time of collision. Neck pain intensity, WAD grade, headache, and no postsecondary education were robust to publication bias. CONCLUSIONS: Using a rigorous process for the identification and extraction of data from a homogenous subset of the prognostic WAD literature, we were able to identify several factors for which information is easy to collect clinically and could provide clinicians with a good sense of prognosis following whiplash injury.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.048 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".