Methodological Issues in Designing a Randomized Controlled Trial for Psychotic Depression: The STOP-PD Study
Bibliographic record
Abstract
The safety and efficacy of electroconvulsive therapy (ECT) has been documented for more than half a century, including among patients with major depressive disorder with psychotic features (MD-Psy) who have failed intensive antidepressant treatment. More recent studies from academic centers that use contemporary ECT standards have demonstrated response and remission rates exceeding 90% among patients with MD-Psy. ABOUT THE AUTHORS Dr. Meyers is professor of psychiatry, Department of Psychiatry, Weill Medical College of Cornell University and New York Presbyterian Hospital, Westchester, NY. Dr. Peasley-Miklus is research associate, Department of Psychiatry, Weill Medical College of Cornell University. 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. 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. Address reprint requests to: Barnett S. Meyers, MD, New York Presbyterian Hospital Westchester, 21 Bloomingdale Road, White Plains, NY 10605; e-mail: bmeyers@med.cornell.edu. Dr. Meyers disclosed no relevant financial relationships. Dr. Peasley-Miklus disclosed relevant financial relationships with Pfizer and Eli Lilly. Dr. Flint disclosed a relevant financial relationship with Pfizer Canada. Dr. Mulsant disclosed relevant financial relationships with Pfizer, Eli Lilly, Forest/Lundbeck, AstraZeneca, Janssen, and Alkermes. Dr. Rothschild disclosed relevant financial relationships with Lilly and Pfizer. This article was supported in part 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 | 0.402 | 0.485 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".