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Record W1579137834 · doi:10.3928/00485713-20060101-09

Challenges in Differentiating and Diagnosing Psychotic Depression

2006· article· en· W1579137834 on OpenAlexaboutno aff

Bibliographic record

VenuePsychiatric Annals · 2006
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsnot available
Fundersnot available
KeywordsPsychiatryDepression (economics)RothschildMedicineMental healthPsychology

Abstract

fetched live from OpenAlex

<P>Major depression with psychotic features (MD-Psy), a disorder with considerable morbidity and mortality, is more common than is generally realized and is encountered frequently in clinical practice. Although studies conducted in both inpatient and outpatient settings have estimated that 16% to 54% of adults with depression are also psychotic, MD-Psy often is not diagnosed accurately because the psychosis may be subtle, intermittent, or concealed leading to a misdiagnosis of nonpsychotic depression.</P> <H4>ABOUT THE AUTHORS</H4> <P>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. Dr. Meyers is professor of psychiatry, Department of Psychiatry, Weill Medical College of Cornell University and New York Presbyterian Hospital, Westchester, NY. 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.</P> <P>Address reprint requests to: Anthony J. Rothschild, MD, University of Massachusetts Medical School, 361 Plantation Street, Worcester, MA 01605; or e-mail <a href="mailto:rothscha@ummhc.org">rothscha@ummhc.org</a>.</P> <P>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. Flint disclosed a relevant financial relationship with Pfizer Canada. Dr. Meyers disclosed no relevant financial relationships. </P> <P>This research was supported in part by National Institutes of Mental Health grants MH 62518, MH 62446, MH 62565, and MH 62624 and the Irving S. and Betty Brudnick Endowed Chair in Psychiatry, University of Massachusetts Medical School.</P>

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.331
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2006
Admission routes1
Has abstractyes

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