Pharmacotherapy of Major Depression with Psychotic Features: What is the Evidence?
Bibliographic record
Abstract
Major depression with psychotic features (MD-Psy) is a significant public health problem. In American studies, between 15% (community sample, ECA) and 25% (inpatient sample) of mixed-age patients who meet criteria for major depressive disorder present with psychotic features. Similarly, in a large European epidemiological study, 19% of noninstitutionalized people ages 19 to 100 with major depression had psychotic features. Among geriatric patients who require hospitalization for the treatment of their depression, the prevalence of MD-Psy may reach 45%. Compared with patients with nonpsychotic depression, patients with MD-Psy exhibit greater impairment following resolution of the depressive episode, greater risk of relapse and recurrence, increased number of suicide attempts, prolonged hospitalizations, increased comorbidity, and increased financial dependence. ABOUT THE AUTHORS Dr. Andreescu is psychiatry resident, Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA. Dr. Mulsant is professor of psychiatry, Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA; clinical director, Geriatric Mental Health Program, Centre for Addiction and Mental Health, Toronto, and professor, Department of Psychiatry, University of Toronto. Dr. Rothschild is Irving S. and Betty Brudnick Professor of Psychiatry, Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA. Dr. Flint is professor, Department of Psychiatry, University of Toronto, and head, Geriatric Psychiatry Program, University Health Network, Toronto, Ontario, Canada; the Geriatric Program and Research Institute, Toronto Rehabilitation Institute, Toronto; and the Toronto General Research Institute, Toronto. Dr. Meyers is professor of psychiatry, Department of Psychiatry, Weill Medical College of Cornell University and New York Presbyterian Hospital, Westchester, NY. Dr. Whyte is assistant professor of psychiatry, Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine. Address reprint requests to: Benoit H. Mulsant, MD, Geriatric Mental Health, CAMH, 1001 Queen Street West, Toronto, Ontario, Canada M6J 1H4; or e-mail benoit_mulsant@camh.net. Dr. Mulsant disclosed relevant financial relationships with Pfizer, Lilly, Forest/Lundbeck, AstraZeneca, Janssen, and Alkermes. Dr. Rothschild disclosed relevant financial relationships with Lilly and Pfizer. Dr. Flint disclosed a relevant financial relationship with Pfizer Canada. Dr. Whyte disclosed a relevant financial relationship with Pfizer. Drs. Andreescu and Meyers disclosed no relevant financial relationships. This research was supported in part by United States Public Health Service grants MH30915, MH48512, MH 62446, MH62518, MH62565, and MH62624 and MH069430 from the National Institute of Mental Health.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".