Exposure to a Motor Vehicle Collision and the Risk of Future Neck Pain: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To summarize the literature that has examined the association between a motor vehicle collision (MVC) related neck injury and future neck pain (NP) in comparison with the population that has not been exposed to neck injury from an MVC. LITERATURE SURVEY: Neck injury resulting from an MVC is associated with a high rate of chronicity. Prognosis studies indicate 50% of injured people continue to experience NP a year after the collision. This is difficult to interpret due to the high prevalence of NP in the general population. METHODOLOGY: We performed a systematic review of the literature using five electronic databases, searching for risk studies on exposure to an MVC and future NP published from 1998 to 2018. The outcome of interest was future NP. Eligible risk studies were critically appraised using the modified Quality in Prognosis Studies (QUIPS) instrument. The results were summarized using best-evidence synthesis principles, a random effects meta-analysis, metaregression, and testing for publication bias was performed with the pooled data. SYNTHESIS: Eight articles were identified of which seven were of lower risk of bias. Six studies reported a positive association between a neck injury in an MVC and future NP compared to those without a neck injury in an MVC. Pooled analysis of the six studies indicated an unadjusted relative risk of future NP in the MVC exposed population with neck injury of 2.3 (95% CI [1.8, 3.1]), which equates to a 57% attributable risk under the exposed. In two studies where exposed participants were either not injured or injury status was unknown, there was no increased risk of future NP. CONCLUSIONS: There was a consistent positive association among studies that have examined the association between MVC-related neck injury and future NP. These findings are of potential interest to clinicians, insurers, patients, governmental agencies, and the courts. LEVEL OF EVIDENCE: I.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".