An Integrated Model of Chronic Whiplash-Associated Disorder
Bibliographic record
Abstract
Synopsis The development of persistent symptoms following whiplash injury from a motor vehicle collision is common and contributes substantially to societal and personal costs. The popular Quebec Task Force classification system of whiplash-associated disorders (WADs) was meant to function as a prognostic and intervention decision aid, but its usefulness has been questioned. Emerging evidence highlights the heterogeneity of WAD by demonstrating physical and psychological impairments that are unique to those who develop persistent symptoms. These impairments are not recognized in the Quebec Task Force classification system. The purpose of this clinical commentary is to describe an integrated model that focuses on how psychological and neurobiological factors interact with, and are influenced by, existing personal and environmental factors to contribute to the development of chronic WAD. The model has been developed through more than 20 years of work in the field, consultation with experts, in-depth synthesis of existing evidence, and new evidence from the authors' own research programs. A subtheme is that a point of convergence currently exists between the psychological, physiological, and social determinants of health literature that can further explain the complex presentation of WAD. The new model is proposed to orient future research toward more interdisciplinary efforts across nontraditional fields, including data scientists and consumers, to clarify the WAD condition. J Orthop Sports Phys Ther 2017;47(7):462-471. Epub 16 Jun 2017. doi:10.2519/jospt.2017.7455.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".