The Effect of Pressure Pain Sensitivity and Patient Factors on Self-Reported Pain-Disability in Patients with Chronic Neck Pain
Bibliographic record
Abstract
The study was conducted to estimate the extent to which pressure pain sensitivity (PPS) and patient factors predict pain-related disability in patients with neck pain (NP), and to determine if PPS differs by gender. Forty-four participants with a moderate level of chronic NP were recruited for this cross sectional study. All participants were asked to complete self-reported assessments of pain, disability and comorbidity and then underwent PPS testing at 4-selected body locations. Pearson`s r w was computed to explore relationships between the PPS measures and the self-reported assessments. Regression models were built to identify predictors of pain and disability. An independent sample t-test was done to identify gender-related differences in PPS, pain-disability and comorbidity. In this study, greater PPS (threshold and tolerance) was significantly correlated to lower pain-disability (r = -.30 to -.53, p≤0.05). Age was not correlated with pain or disability but comorbidity was (r= 0.42-.43, p≤0.01). PPS at the 4-selected body locations was able to explain neck disability (R(2)=25-28%). Comorbidity was the strongest predictor of neck disability (R(2) =30%) and pain (R(2)=25%). Significant mean differences for gender were found in PPS, disability and comorbidity, but not in pain intensity or rating. This study suggests that PPS may play a role in outcome measures of pain and disability but between-subject comparisons should consider gender and comorbidity issues.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".