Pain threshold reflects psychological traits in patients with chronic pain: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Chronic pain enhances sensory sensitivity and induces the biased development of psychological traits such as depression and pain catastrophizing, leading to the formation of heterogeneous conditions. Fluctuations in the sensory-related thresholds of non-injured sites (with normal peripheral tissue) in patients with chronic pain are thought to be related to central sensitization. The objectives of this study were to analyze the association between pain tolerance thresholds (PTTs) in non-injured sites and the psychological traits of patients with chronic pain and to evaluate the usefulness of PTT measures in assessments of pathological conditions related to chronic pain. METHODS: This study included 57 patients with chronic pain. The PTTs were measured in non-injured sites with quantitative sensory testing (QST) with electrical stimulation and then classified with cluster analysis. The Short-Form McGill Pain Questionnaire was used to subjectively assess pain in the injured sites. The Minnesota Multiphasic Personality Inventory (MMPI) was used to assess the patients' psychological traits. RESULTS: Based on the cluster analysis of PTTs, the patients were classified into a High-Sensitivity group and an Others group consisting of the remaining patients. The results of the MMPI profiles showed that the High-Sensitivity group included significantly more patients with the Neurotic Triad pattern and no patients with the Conversion V pattern. The scores of the hypochondriasis and hysteria scales were significantly lower in the High-Sensitivity group than in the Others group. CONCLUSIONS: This study indicated that patients with chronic pain can be classified according to PTTs in non-injured sites and suggests that patients with High-Sensitivity have characteristic psychological traits. Assessment of PTTs in non-injured sites would be useful for evaluating the psychological condition of patients with chronic pain.
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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 | 0.002 |
| 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 | 0.001 |
| 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".