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Record W859807672 · doi:10.1177/104012371502700209

Canadian Network for Mood and Anxiety Treatments (Canmat) Consensus Recommendations for Functional Outcomes in Major Depressive Disorder

2015· article· en· W859807672 on OpenAlexaffabout
Raymond W. Lam, Sagar V. Parikh, Erin E. Michalak, Carolyn S. Dewa, Sidney H. Kennedy

Bibliographic record

VenueAnnals of Clinical Psychiatry · 2015
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsMajor depressive disorderPsychological interventionConceptualizationMoodAnxietyPsychologyClinical trialPsychiatrySystematic reviewClinical psychologyMEDLINEMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Functional recovery is increasingly recognized as a priority in the treatment of major depressive disorder (MDD), by both clinicians and patients. However, symptom improvement remains the focus of traditional clinical trials for MDD and of the regulatory approval process for new medications and other interventions. Many studies have shown that functional outcomes do not always correspond to symptom-based outcomes. METHODS: Representatives from clinical practice, professional societies, academia, industry, and government were invited by the Canadian Network for Mood and Anxiety Treatments to develop recommendations for the conceptualization and measurement of functional outcomes in clinical trials of MDD. RESULTS: Definitions and conceptual frameworks to guide assessment of functioning are described, as well as research methodology applicable to the broad spectrum of treatments for MDD. Examples are given for validated instruments, including patient-reported outcome measures. Strategies for knowledge translation and dissemination are suggested and consensus recommendations summarized. CONCLUSIONS: As the societal burden and financial costs of MDD continue to escalate, so does the need for evidence-based and cost-effective interventions that demonstrate improvement in functioning. Routine assessment of functional outcomes will benefit not only individuals with MDD but also diverse stakeholders concerned about the efficacy and cost-effectiveness of interventions.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.134
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.165
GPT teacher head0.446
Teacher spread0.281 · 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

Citations62
Published2015
Admission routes2
Has abstractyes

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