Investigating the Depression Status in Patients with Upper Limb Pain
Bibliographic record
Abstract
Introduction: Depression is considered as the most common psychological problem in societies. Depression, anxiety disorders, and substance abuse are more common in patients with pain compared to the general population. In this study, the state of depression in patients with upper limb pain with radiculopathy or without paraclinical signs of radiculopathy has been investigated. Material and Methods: We conducted the depression status in patients with upper limb pain with and without radiculopathy in a descriptive cross-sectional study in Sari in 2017. Beck Depression Inventory (BDI-II), Short Form (36) Health Survey (SF-36), short-form McGill pain questionnaire was used to evaluate the status of major depressive disorder, health status and quality of life, and severity of pain in them, respectively. The data was analyzed by SPSS 22. Results: From 120 patients with the mean age 44.97±9.77 years, 19% had mild depression, 18% moderate depression and 11% severe depression. The mean score of SF36 was 29.94±6.86. The mean scores of McGill pain scale was 13.31±6.02. The mean depression score had a significant difference between the two groups studied (P=0.04). The McGill pain score had also a significant difference between the two study groups (P=0.012). The mean score of SF36 had no significant difference in both groups (P=0.41).Conclusion: The depression score and the prevalence of moderate and severe depression were also higher in patients with chronic upper limb pain with cervical radiculopathy than in patients without cervical radiculopathy.
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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) | 0.001 | 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".