Multiple Pain Complaints in Patients With Major Depressive Disorder
Bibliographic record
Abstract
OBJECTIVE: To characterize the co-existence of multiple pain-related complaints in patients enrolled in a series of pharmaceutical company drug trials for the treatment of Major Depressive Disorder (MDD). METHOD: Pooled 'blinded' data from 2191 patients enrolled in randomized, multicenter, double-blind placebo-controlled studies for the treatment of MDD were analyzed. Painful symptoms were assessed using the seven pain symptoms subset of the Somatic Symptoms Inventory: 'Headache,' 'Pain in lower back,' 'Neck pain,' 'Pain in joints,' 'Soreness in muscles,' 'Pain in heart or chest,' and 'Pain or cramps in abdomen.' The 17-item Hamilton Depression Rating Scale (HAMD) was used to assess severity of depression. RESULTS: Of those meeting the study entry criteria (total HAMD score >or=15), 25% reported no pain complaints and 18% reported 1 pain compliant; the majority (57%) of patients reported the co-existence of multiple pain-related complaints, with 14%, 12%, 11%, 11%, 7%, and 3% of patients reporting 2, 3, 4, 5, 6 and 7 different pain symptoms, respectively. The number of pain-related symptoms experienced was moderately related to severity of depression (r = 0.35), with the most common pain symptom combinations being among headaches, lower back pain, neck pain, pain in joints, and soreness in muscles. CONCLUSIONS: This study supports pain as a component feature of MDD. The number of comorbid pain-related complaints, which generally increased as a function of depressive severity, should be considered in the diagnosis of depression, planning of treatment strategies, and measurement of treatment outcome.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".