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Record W4280575475 · doi:10.1007/s40473-022-00247-y

Contributors of Functional Impairment in Major Depressive Disorder: a Biopsychosocial Approach

2022· article· en· W4280575475 on OpenAlexaff
Troy K. Chow, Christopher R. Bowie, Michael Morton, Aleksandra Lalovic, Shane McInerney, Sakina J. Rizvi

Bibliographic record

VenueCurrent Behavioral Neuroscience Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsQueen's UniversityCentre for Addiction and Mental HealthUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsBiopsychosocial modelMajor depressive disorderDepression (economics)Functional impairmentPsychologyClinical psychologyPsychiatryCognitionMedicine

Abstract

fetched live from OpenAlex

This narrative review summarizes the current evidence regarding functional impairment in MDD and the factors contributing to it. Major depressive disorder (MDD) is a leading cause of disability and is associated with substantial economic burden, largely due to the functional impairment common among MDD patients. Despite the prevalence of functional impairment in MDD, it has not typically been investigated as a primary treatment outcome in MDD until recently, as treatment studies have largely focused on symptoms. Notably, studies consistently demonstrate the divergent trajectories of improvement of depression symptoms and functional impairment. Furthermore, the most consistent findings point to MDD symptom severity, cognitive deficits, sleep, fatigue, low energy, and social isolation as key contributors to functional outcomes in MDD. There is currently a paucity of data regarding neurobiological mechanisms of functional impairment. The findings from published literature are organized into a proposed working biopsychosocial model of functional impairment in MDD that highlights the strengths and existing research gaps in the field. The implications of these findings on depression treatment strategies are also discussed. Our proposed biopsychosocial model of functioning in depression may serve to define an individual’s “functional impairment profile,” which can be used to identify targeted and personalized treatments for depression, and lead to improved outcomes.

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 score0.031
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.334
Teacher spread0.289 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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