Associations Between Pain drawing and Psychological Characteristics of Different Body Region Pains
Bibliographic record
Abstract
BACKGROUND: Pain drawings have frequently been used for documentation of pain and a convenient diagnosis tool. Pain drawings were found to be associated with psychological states in chronic patients with low back pain. Few researchers have investigated pain drawings except in low back pain. The aim of this study was to investigate the pain, pain drawings, psychological characteristics, and pain interference in the head, neck-shoulder (NS), and low-back/lower-limb (LB-LL) regions among patients with chronic pain. METHODS: We included a total of 291 patients with new chronic pain (headache, 62; NS pain, 87; LB-LL pain, 142). The pain drawings and scores of 10-cm Visual Analogue Scale (VAS), Hospital Anxiety and Depression Scale (HADS), Pain Catastrophizing Scale (PCS), Short-Form McGill Pain Questionnaire (SF-MPQ), and Pain Disability Assessment Scale (PDAS) were extracted from medical records. A subset of 60 pain drawings was scored by senior and junior evaluators to assess inter-rater agreement. We investigated the correlation between pain drawings and VAS, HADS, PCS, SF-MPQ, and PDAS in each body region group at the initial visit. Moreover, almost all patients received nonsurgical treatment as a follow-up and were investigated using VAS after treatment. RESULTS: The reliability of pain drawings was substantial with an interevaluator reliability in headache, NS, and LB-LL pain. Nonorganic pain drawings were associated with psychological disturbances in NS and LB-LL pain, but not headache. Poor outcomes were associated with nonorganic drawings in LB-LL pain, but not in the case of headache or NS pain. CONCLUSIONS: Our results suggest that the characteristics of patients with nonorganic drawings differ according to body regions.
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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 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".