A Systematic Literature Review of Physical Prognostic Factors for the Development of Late Whiplash Syndrome
Bibliographic record
Abstract
STUDY DESIGN: Systematic Review. OBJECTIVE: To summarize evidence concerning physical prognostic factors for development of Late Whiplash Syndrome (LWS). SUMMARY OF BACKGROUND DATA: There have been 3 previous systematic reviews of prognosis of whiplash with conflicting findings. The Quebec Task Force concluded that high priority should be given to determining prognostic factors. Subsequently their review was updated by Cote et al (Spine 2001;26:E445-58) and most recently by Scholten-Peeters et al (Pain 2003;104:303-22). METHODS: We searched electronic databases from their inception to August 2006 using a prespecified search strategy. We included prospective cohort and case control studies that studied physical prognostic factors at baseline. Two independent reviewers selected articles, extracted data, and assessed quality. Meta-analysis was not performed due to the heterogeneity between studies. Instead, levels of evidence were generated by grouping similar findings from cohorts. RESULTS: Thirty-eight articles from 26 cohorts were reviewed. The majority of articles (25 of 38) were rated as low quality. No studies were rated as high quality. Only a minority of studies used validated prognostic measures and/or outcome measures. High initial neck pain intensity, neck pain related disability, and cold hyperalgesia all had moderate evidence for an association with the development of LWS. No factor was rated as having strong evidence. CONCLUSION: Pain has a central role to play as a prognostic factor for the development of LWS. Other physical factors commonly used in the clinical setting showed inconclusive evidence for their influence on prognosis. There is a need for improved quality of studies with consistent use of validated measures of all categories of prognostic factors and outcome. This may then provide a clearer understanding of prognosis of Whiplash Associated Disorders and therefore facilitate effective management of this costly problem.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".