Using Measurement Strategies to Identify and Monitor Residual Symptoms
Bibliographic record
Abstract
Article AbstractMajor depressive disorder (MDD) is a persistent, pervasive, and chronic disorder that significantly affects patients†functioning and quality of life. Most patients treated for MDD continue to have residual symptoms after acute treatment with pharmacotherapy. One of the most commonly encountered residual symptoms is cognitive dysfunction, which substantially affects patient outcomes. While antidepressant monotherapy is an effective first-line treatment for some patients with MDD, patients with residual symptoms (eg, cognitive dysfunction) will require an additional treatment intervention such as augmentation or switch to an alternative treatment strategy. Measurement-based care has been demonstrated to improve patient outcomes in MDD. The clinical importance of cognitive dysfunction in MDD invites the need to probe, screen, and measure the extent of cognitive impairment. (J Clin Psychiatry 2013;74:14-18) From the Department of Psychiatry, University of Toronto, and the Mood Disorders Psychopharmacology Unit, University Health Network, Toronto, Ontario, Canada. This article is derived from the planning teleconference series "Depression: Managing the Full Range of Symptoms to Achieve Lasting Remission," which was held in May and June 2013 and supported by an educational grant from Takeda Pharmaceuticals International, Inc., US Region and Lundbeck. Dr McIntyre has served on the advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, France Foundation, GlaxoSmithKline, Janssen-Ortho, Lundbeck, Merck, Organon, Pfizer, and Shire; has served on the speakers bureaus for AstraZeneca, Eli Lilly, Janssen-Ortho, Lundbeck, Merck, and Pfizer; has received grant/research support from AstraZeneca, Eli Lilly, Janssen-Ortho, Lundbeck, the National Alliance for Research on Schizophrenia and Depression, the National Institutes of Mental Health, Pfizer, Shire, and the Stanley Medical Research Institute; and has participated in CME activities for AstraZeneca, Bristol-Myers Squibb, CME Outfitters, Eli Lilly, France Foundation, I3CME, Merck, Optum Health, and Pfizer. Corresponding author: Roger S. McIntyre, MD, 399 Bathurst St, MP 9-325, Toronto, Ontario, M5T 2S8, Canada (roger.mcintyre@uhn.on.ca). doi:10.4088/JCP.12084su1c.03 © Copyright 2013 Physicians Postgraduate Press, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".