ALEXITHYMIA IN CHRONIC AND ACUTE NON-SPECIFIC CERVICAL AND BACK PAIN
Bibliographic record
Abstract
Alexithymia (difficulty of verbalization of sensations) is currently considered as a factor aggravating the course of somatic diseases, including musculoskeletal pain. Objective: to determine the frequency of occurrence of alexithymia in patients with nonspecific pain in the neck and back and its association with the clinical features of pain. Contingent and examination methods: 156 patients aged 18-50 years (mean age 38.2 ± 10.1 years), 89 women and 67 men who applied to medical institutions for axial pain (paravertebral localization of pain) in the neck, thoracic region spine and lumbar region. Research methods: Toronto alexithymia scale, visual analogue scale (VAS) of pain, VAS influence of pain on disability, VAS life satisfaction, hospital scale of depression and anxiety, index of widespread pain. Results: patients with alexithymia and acute pain had statistically significantly higher rates for VAS (pain intensity), disability index and hospital anxiety and depression scale, and significantly lower life satisfaction index than non-alexithymic patients. The same pattern was found in the subgroup of patients with chronic pain. Conclusions: comorbidity of alexithymia, acute and chronic pain, anxiety and depression makes it advisable to register this phenomenon in the clinic to predict the disease and to develop further ways to overcome the problem.
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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 | 0.001 |
| 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 | 0.000 | 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".