Psychological Traits of Patients With Depression Comorbid With Chronic Pain: Are Complaint and Competitive Tendency Related to Pain?
Bibliographic record
Abstract
BACKGROUND: Modern-Type Depression (MTD) is a category of depression that has been studied mainly in Japan; however, no study has attempted to determine its relation to chronic pain. AIM: To determine possible associations between psychological traits related to MTD and the chronic pain of patients at psychiatric clinics. METHOD: Two hundred and twenty-one first time patients who visited the psychiatric clinic at a Japanese university medical center or an associated clinic were enrolled. The Hamilton Depression Rating Scale was used to measure depressive symptoms. The 22-item Tarumi's Modern-Type Depression Trait Scale (TACS-22), Achievement Motive, and 20-item Toronto Alexithymia Scale were used to assess psychological traits related to depression and chronic pain. The clinical diagnosis of each patient was confirmed by use of the Structured Clinical Interview for DSM-IV Axis I Disorders, administered by experienced specialists. The medians of the psychological traits identified were compared between patients with or without chronic pain. Analysis was also done of patients with Major Depressive Disorder (MDD). RESULT: Of the 221 patients, 139 had chronic pain. Patients with chronic pain had more severe depressive symptoms, Alexithymia, and high scores for the complaint trait of MTD. Seventy-three of the 221 patients met the criteria for MDD (53 had chronic pain). Patients with MDD comorbid with chronic pain had a higher competitive achievement score, severe depression, and difficulty identifying feelings. CONCLUSION: Complaint and competitive traits were shown to be related to chronic pain in psychiatric settings. Further study will allow us to design multidimensional approach for patients suffering from depression.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".