Research Assessment of Patients With Psychotic Depression: The STOP-PD Approach
Bibliographic record
Abstract
Major depression with psychotic features (psychotic depression) is a severe, disabling disorder. Compared with nonpsychotic major depression, psychotic depression has been associated with greater severity of depressive symptoms, greater functional impairment, increased risk of suicide, lower rate of recovery, increased risk of depressive relapse and recurrence, and more frequent hospital admissions. There are significant challenges in conducting a treatment study of psychotic depression, including the recruitment, retention, and assessment of patients. ABOUT THE AUTHORS 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. Schaffer is assistant professor, Department of Psychiatry, University of Toronto, and Head, Mood Disorders Program, Sunnybrook and Women’s College Health Sciences Centre, Toronto. Dr. Meyers is professor of psychiatry, Department of Psychiatry, Weill Medical College of Cornell University and New York Presbyterian Hospital, Westchester, NY Dr. Rothschild is Irving S. and Betty Brudnick Professor of Psychiatry, Department of Psychiatry, University of Massachusetts Medical School, Worcester, MA. 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. Address reprint requests to: Alastair J. Flint, MB, FRCPC, FRANZCP, Toronto General Hospital, 200 Elizabeth Street, 8 Eaton North, Room 238, Toronto, Ontario, Canada, M5G 2C4; or e-mail alastair.flint@uhn.on.ca. Dr. Flint disclosed a relevant financial relationship with Pfizer Canada. Dr. Schaffer disclosed a relevant financial relationship with Eli Lilly Canada. Dr. Rothschild disclosed relevant financial relationships with Lilly and Pfizer. Dr. Mulsant disclosed relevant financial relationships with Pfizer, Eli Lilly, Forest/Lundbeck, AstraZeneca, Janssen, and Alkermes. Dr. Meyers disclosed no relevant financial relationships. This article was supported by United States Public Health Service grants MH 62446, MH 62518, MH 62565, and MH 62624 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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".