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Record W4402909784 · doi:10.1016/j.ssmmh.2024.100350

Treatment resistance in schizophrenia and depression as an interactive kind: Mapping the development of a classification through Meta-Narrative review

2024· article· en· W4402909784 on OpenAlexafffund
Leighton Schreyer, Csilla Kalocsai, Oshan Fernando, Melanie Anderson, Vanessa Lockwood, Sophie Soklaridis, Gary Remington, Araba Chintoh, Suze Berkhout

Bibliographic record

VenueSSM - Mental Health · 2024
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsFoothills Medical CentreUniversity of TorontoUniversity Health NetworkHealth Sciences CentreCentre for Addiction and Mental HealthSunnybrook Health Science Centre
FundersDepartment of Psychiatry, University of TorontoUniversity of TorontoTemerty Faculty of Medicine, University of TorontoUniversity Health Network
KeywordsSchizophrenia (object-oriented programming)NarrativePsychologyNarrative reviewDepression (economics)Resistance (ecology)PsychotherapistMeta-analysisCognitive psychologyClinical psychologyPsychiatryMedicineLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Despite ongoing attempts to delineate and name treatment resistance (TR) in psychiatry, the term is increasingly deployed across diagnostic categories. Still, what it is that constitutes TR remains unclear and in flux. Through a meta-narrative review, we construct a sociohistorical map of the concept of TR as it is employed in schizophrenia (TRS) and major depressive disorders (TRD). We track debates about TR, identify underlying assumptions and influencing factors that shape how the concept has evolved over time, and consider the intended and unintended consequences of its conceptualization. We develop our findings as three unique threads that, braided together, offer insight into TR as an interactive kind. Each thread analyzes and plays with the notion of heterogeneity , which arises in the literature as both a theme and a problem to be solved. Thread one looks at prevailing controversies surrounding the definition of TR. Here, heterogeneity arises in relation to how TR is delineated. We also consider the notion of “pseudoresistance,” a novel concept that functions to manage and contain heterogeneity, defining the boundaries of TR through its exclusions. Thread two explores the range of actors whose interests and practices are coordinated to shape TR as a concept: the pharmaceutical industry, academic psychiatry, clinicians, and health systems. Each group has its own interests and orientations: a heterogenous range of actors contributing to the thing that TR is. Thread three examines the intended and unintended consequences that attempts to conceptualize TR have yielded, including a reification of the biomedical paradigm and the personification of TR. This paper offers a systematic approach to thinking about similarities, differences, particularities and tensions embedded within TR to understand the politics and possibilities of the concept. • Meta-Narrative review is a valuable methodology for synthesizing disparate ideas. • Definitions of Treatment Resistance (TR) in psychiatry are highly variable. • The heterogeneity of TR is reflective of its ontological multiplicity. • Definitions of TR reflect multiple interests and perspectives from different actors. • There are intended and unintended consequences of trying to conceptualize TR.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.377

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 head0.125
GPT teacher head0.426
Teacher spread0.301 · 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 designQualitative
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

Citations0
Published2024
Admission routes2
Has abstractyes

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