A narrative review on cervical interventions in adults with chronic whiplash-associated disorder
Bibliographic record
Abstract
Introduction: Whiplash injuries are common in society, but clinical interventions are inconclusive on the most effective treatment. Research and reviews have been completed with the goal of determining clinical interventions that are effective for whiplash injuries and disorders, but literature has not recently been summarised on best practices for cervical spine interventions for adults with chronic whiplash. Purpose: The objective of this narrative review is to update and expand on previous works, to provide recommendations for clinical interventions and future research in the area of cervical spine rehabilitation for adults with chronic whiplash-associated disorder. Method: The Arskey and O'Malley methodology was used for this narrative review. CINHAL, EMBASE, Medline, PsychInfo, Scopus, Web of Science, as well as grey literature, were searched from 2003 to April 2017. Two reviewers screened titles and abstracts for relevance to the review, and content analysis summarised the study findings. A total of 14 citations were included in the final review. Findings: Exercise-based interventions targeted at the cervical spine appear most beneficial for adults with chronic whiplash-associated disorder (WAD). Invasive interventions still require more rigorous studies to deem their effectiveness for this population. Conclusion: Further research is required to investigate and determine clinically relevant results for cervical spine intervention in patients with chronic WAD.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".