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Record W4288682769 · doi:10.4088/jcp.lu21112ah1

Patient Functioning and Life Engagement

2022· article· en· W4288682769 on OpenAlexaff
Christoph U. Correll, Zahinoor Ismail, Roger S. McIntyre, Roueen Rafeyan, Michael E. Thase

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of TorontoUniversity of CalgaryUniversity Health Network
Fundersnot available
KeywordsPerspective (graphical)PsychologyWishMental illnessPsychotherapistPsychiatryMental healthSociology

Abstract

fetched live from OpenAlex

Definitions of treatment success used in clinical trials of medications for serious mental illness have generally focused on reduction in symptoms assessed via observer-rated instruments such as the Montgomery-Asberg Depression Rating Scale and the Hamilton Depression Rating Scale in MDD and the Positive and Negative Syndrome Scale (PANSS) and Brief Psychiatric Rating Scale in schizophrenia. In recent years, there has been a shift toward incorporating outcomes into clinical research that are patient-reported and reflect outcomes and goals that are meaningful to the patient. These outcomes include aspects of functioning (eg, activities of daily living and role fulfillment), as well as life engagement, which interacts with symptomatic and functional outcomes and encompasses aspects such as motivation and vitality. In a recent roundtable meeting, a panel of 5 experts discussed life engagement and its relationship to symptoms and functioning in patients with major depressive disorder (MDD) and schizophrenia. This Academic Highlights summarizes their discussion.

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)0.007
metaresearch head score (Gemma)0.027
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.381
GPT teacher head0.517
Teacher spread0.137 · 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
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

Citations13
Published2022
Admission routes1
Has abstractyes

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