Influence of the Cognitive and Emotional Status of Patients with Chronic Pain on Treatment Success (Reduction in Pain Intensity and Adherence to Pharmacotherapy): A Prospective Study
Bibliographic record
Abstract
This prospective study aimed to determine the cognitive and emotional status among patients with chronic pain and to examine the potential influence on the treatment success, measured by the reduction in pain intensity and adherence to pharmacotherapy. A total of seventy patients were followed for two months. The results of the comparison between patients who did and did not follow the physician’s instructions regarding adherence to pharmacotherapy showed a significant difference in cognitive status and a reduction in pain intensity. Patients who followed the physician’s instructions on taking analgesics had significantly higher scores on the Montreal Cognitive Assessment (MoCA) of cognitive status and a substantially higher reduction in pain intensity. Scores on the MoCA test provide statistically significant indications regarding patients’ decision to follow instructions regarding adherence to pharmacotherapy. Scores on the MoCA test, anxiety, age, and pain intensity (measured with a numeric rating scale—NRS) on admission were identified as potential predictors for the reduction in pain intensity. The linear regression model was statistically significant (χ2 = 40.0, p < 0.001), explained between 43.5% and 61.1% of variance regarding the reduction in pain intensity. The findings of this study show that cognitive status, measured with MoCA, and emotional status, measured with the Depression, Anxiety, and Stress Scale (DASS-21), significantly influence the reduction in pain intensity and adherence to pharmacotherapy. The results suggest that cognitive and emotional status may be potential predictors of treatment success. This finding points to the importance of a biopsychosocial approach in the treatment of chronic pain, where an important emphasis can be placed on the psychosocial determinants of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".