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Record W1193154704 · doi:10.1017/s2045796015000682

Major depressive disorder: reification and (maybe) rheostasis

2015· article· en· W1193154704 on OpenAlexaff
Scott B. Patten

Bibliographic record

VenueEpidemiology and Psychiatric Sciences · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsSouth Health CampusUniversity of Calgary
Fundersnot available
KeywordsReification (Marxism)Biopsychosocial modelMajor depressive disorderPsychologyContext (archaeology)Perspective (graphical)Set (abstract data type)Depression (economics)PsychotherapistPsychiatryClinical psychologyComputer scienceCognitionArtificial intelligence

Abstract

fetched live from OpenAlex

The heterogeneity of clinical syndromes subsumed by diagnostic criteria for major depressive disorder (MDD) is regarded by some as a reason to abandon or modify the criteria. However, heterogeneity may be unavoidable because of the biopsychosocial complexity of depression. MDD may be characterised by complexities that cannot be distilled down to any brief set of diagnostic criteria. Psychiatrists and psychiatric epidemiologists may need to revise their expectations of this diagnosis in order to avoid over-estimating its ability to guide the selection of treatments and prediction of prognosis. An opposing perspective is that of reification, in which the diagnosis is viewed as being more real than it really is. The concept of rheostasis may help to explain some features of this condition, such as why major depressive episodes sometimes seem understandable or even adaptive (e.g. in the context of bereavement) whereas at other times such episodes are inexplicable and maladaptive.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptScience and technology studies
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.122
GPT teacher head0.357
Teacher spread0.236 · 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

Labeled directly by 2 models reading the full record.

Science and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical · Commentary

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

Citations6
Published2015
Admission routes1
Has abstractyes

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