Alternative diagnostic criteria for major depressive disorder in patients with chronic pain
Bibliographic record
Abstract
Chronic pain is associated with high rates of major depressive disorder (MDD), but somatic symptoms caused by pain may complicate the diagnosis of MDD. Different methods to address this issue include the adoption of an inclusive approach to diagnosis (i.e. including all symptoms when assessing MDD, regardless of their presumed cause), an etiologic approach (i.e. disregarding symptoms that are caused by medical problems), and a substitutive approach (i.e. replacing somatic symptoms with non-somatic alternatives). In this study, 129 patients with chronic pain (56 men and 73 women) underwent semi-structured interviews addressing 23 individual symptoms of MDD. Detailed probing was undertaken into patients' perceptions of the causes of those symptoms that could potentially be brought on by pain. We found that the prevalence of MDD was highest with the inclusive diagnostic method (35.7%), lowest with an etiologic approach that discounted symptoms based on patient attributions (19.4%), and intermediate with the substitutive method (30.3%). Although some symptoms, such as insomnia, fatigue, and difficulty concentrating, were reported by 34--53% of the patients who did not meet criteria for MDD, they were still more common among those who did (85--94%, P<0.001). Patients who met criteria for MDD with the inclusive method, but who did not meet criteria using the etiologic method, had Beck Depression Inventory scores (M=24.5) that were comparable to those of patients who were consistently classified with MDD across methods (M=25.6). These scores were much higher than those of patients who were consistently classified without MDD (M=13.8, P<0.001). In conclusion, excluding criterion symptoms that patients attribute to pain can reduce the observed prevalence of MDD by about 45%. However, this method introduces a problem of false-negative diagnoses that appears to be more significant than the problem of false positives associated with the inappropriate inclusion of somatic symptoms.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".