Exploration of Undertreatment and Patterns of Treatment of Depression in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Depression is a common comorbid condition with multiple sclerosis (MS). Historically, however, it has been undertreated. Little is known about the characteristics of those who receive, or do not receive, treatment for depression in the MS population. This study evaluated depression treatment in patients with MS, associated patient characteristics, and probable determinants of antidepressant drug use in those with and without depression. METHODS: A total of 152 patients with MS completed questionnaires and the Structured Clinical Interview for DSM-IV-TR (SCID) to determine depression status. Tabular analyses and a binary regression model were used to identify patient characteristics associated with antidepressant drug use. RESULTS: Of participants with major depression according to the SCID, 65% were taking antidepressant medications. With adjustment for successful treatment (antidepressant drug use by those not currently depressed and currently depressed), the prevalence of treated depression increased to 85.7%. Of those receiving treatment for depression, 19% were receiving nonpharmacologic treatment alone, 38% were taking antidepressant drugs only, and 44% were receiving both pharmacologic and nonpharmacologic treatments. Demographic and clinical variables were not statistically significantly associated with antidepressant drug use in those with depression. CONCLUSIONS: A large proportion of participants with depression in MS are now receiving treatment, a change from previous reports. The adequacy of treatment has become a bigger question because many of the treated patients continued to have depressive symptoms. Further research is needed to identify ways to achieve better outcomes for depression.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".