Exposure to a motor vehicle collision and the risk of future back pain: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to summarize the evidence for the association between exposure to a motor vehicle collision (MVC) and future low back pain (LBP). LITERATURE SURVEY: Persistent low back pain (LBP) is a relatively common complaint after acute injury in a MVC, with a reported 1 year post-crash prevalence of at least 31 % of exposed individuals. Interpretation of this finding is challenging given the high incidence of LBP in the general population that is not exposed to a MVC. Risk studies with comparison control groups need to be examined in a systematic review. METHODOLOGY: A systematic search of five electronic databases from 1998 to 2019 was performed. Eligible studies describing exposure to a MVC and risk of future non-specific LBP were critically appraised using the Quality in Prognosis Studies (QUIPS) instrument. The results were summarized using best-evidence synthesis principles, a random effects meta-analysis and testing for publication bias. SYNTHESIS: The search strategy yielded 1136 articles, three of which were found to be at low to medium risk of bias after critical appraisal. All three studies reported a positive association between an acute injury in a MVC and future LBP. Pooled analysis of the results resulted in an unadjusted relative risk of future LBP in the MVC-exposed and injured population versus the non-exposed population of 2.7 (95 % CI [1.9, 3.8]), which equates to a 63 % attributable risk under the exposed. CONCLUSIONS: There was a consistent positive association in the critically reviewed literature that investigated the risk of future LBP following an acute MVC-related injury. For the patient with chronic low back pain who was initially injured in a MVC, more often than not (63 % of the time) the condition was caused by the MVC. These findings are likely to be of interest to clinicians, insurers, patients, governments and the courts. Future studies from both general and clinical populations would help strengthen these results.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".