Pressure Pain Threshold Testing Demonstrates Predictive Ability in People With Acute Whiplash
Bibliographic record
Abstract
STUDY DESIGN: Longitudinal cohort study. OBJECTIVES: To determine whether pressure pain threshold (PPT), tested at 2 standardized sites, could provide additional prognostic ability to predict short-term outcomes in people with acute whiplash, after controlling for age, sex, and baseline pain intensity. BACKGROUND: PPT may be a valuable assessment and prognostic indicator for people with whiplash-associated disorder. The extent to which PPT can predict short-term disability scores has yet to be explored in people with acute (of less than 30 days in duration) whiplash-associated disorder in a clinical setting. METHODS: Eligible patients were recruited from community-based physiotherapy clinics in Canada. Baseline measurements included PPT, as well as pain intensity, age, and sex. Neck-related disability was collected with the Neck Disability Index 1 to 3 months after PPT testing. Multiple linear regression models were constructed to evaluate the unique contribution of PPT in the prediction of follow-up disability scores. RESULTS: A total of 45 subjects provided complete data. A regression model that included sex, baseline pain intensity, and PPT at the distal tibialis anterior site was the most parsimonious model for predicting short-term Neck Disability Index scores 1 to 3 months after PPT testing, explaining 38.6% of the variance in outcome. None of the other variables significantly improved the predictive power of the model. CONCLUSION: Sex, pain intensity, and PPT measured at a site distal to the injury were the most parsimonious set of predictors of short-term neck-related disability score, and represented promising additions to assessment of traumatic neck pain. Neither age nor PPT at the local site was able to explain significant variance beyond those 3 predictors. Limitations to interpretation are addressed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".