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Record W4253817207 · doi:10.1017/cbo9780511547201

Psychotic Depression

2007· book· en· W4253817207 on OpenAlexaff
Conrad M. Swartz, Edward Shorter

Bibliographic record

VenueCambridge University Press eBooks · 2007
Typebook
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDepression (economics)PsychiatryPsychotic depressionMedical illnessPsychologyPsychotherapistMedicineClinical psychologyPsychosisIllness behavior

Abstract

fetched live from OpenAlex

Psychotic depression is a distinct and acute clinical condition along the spectrum of depressive disorders. It can manifest itself in many ways and often induces very violent and suicidal behavior. This book aims to help clinical practitioners and trainees describe their observations of psychotic depression, formulate treatment, and express expectations of recovery from illness. It focuses on all facets of the disorder, from clinical history to coverage of diagnostic and treatment protocols. Medical readers of this book will come away able to diagnose and readily treat psychotic depression and thus will be able to serve their patients better. Non-physician readers will come away with the message that this is a terrible illness, but there is hope.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.072
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0720.054

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 head0.023
GPT teacher head0.243
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations29
Published2007
Admission routes1
Has abstractyes

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