Differential Predictors of Pain and Disability in Patients with Whiplash Injuries
Bibliographic record
Abstract
The psychological predictors of pain and disability were examined in a sample of people who sustained whiplash injuries during rear-end motor vehicle accidents. Sixty-five patients referred to a specialty pain clinic with a diagnosis of whiplash injury completed measures of depression, anxiety, catastrophizing, pain and perceived disability. Regression analysis revealed that psychological variables accounted for 18% of the variance in pain ratings. The magnification subscale of the Pain Catastrophizing Scale was the only variable that contributed significant, unique variance to the prediction of pain. Psychological variables accounted for 37% of the variance in perceived disability scores. In the latter analysis, however, none of the independent variables contributed significant, unique variance to the prediction of perceived disability. Psychological variables accounted for significant variance in disability ratings, even when controlling for pain intensity. Discussion focuses on the need to draw clearer distinctions between determinants of pain and disability, and directions for interventions aimed at minimizing disability following whiplash injury are suggested.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".