Predicting the Intensity of Pain in Patients With Chronic Pain Based on Alexithymia: The Mediating Role of the Behavioral Inhibition System
Bibliographic record
Abstract
ObjectivesThis study aimed to predict the intensity of pain based on the amount of alexithymia in patients with chronic pain, and in this relationship, the behavioral inhibition system has a mediating role.Methods The present study has a correlational design and carried out with a structural equation modeling approach.The statistical population comprised all 20-60 years old patients with chronic pain who had referred to Mahan Clinic and the physical medicine and rehabilitation clinic of Arman in Tehran, from spring to autumn of 2017.Of them, 488 patients who had experienced the musculoskeletal pain for at least 3 months, were chosen purposefully and asked to respond the Toronto Alexithymia Scale (TAS-20), the inhibition/activation system scale, and Numeric Rating Scale (NRS).The analysis was done in SmartPLS. ResultsThe intensity of pain had a positive relationship with inhibition system (P<0.001)and fight/ flight system (P<0.05).Alexithymia had the positive a relationship with inhibition system (P<0.001).Alexithymia (t=6.34,β=0.29), inhibition system (t=3.77,β=0.17), and fight/ flight system (t=4.26,β=0.18) could explain the variance of the pain intensity.Alexithymia could explain the activity of inhibition system (t=8.03,β=0.30) and the behavioral activation system (t=2.83,β=-0.14). ConclusionThe relationship between alexithymia and the intensity of pain was not a simple direct relationship, but the inhibition system with its avoidance behavior output could affect this relationship.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".