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Record W2904664036 · doi:10.1186/s12888-018-1969-3

The “Clinician’s illusion” and the epidemiology, diagnosis and treatment of depressive disorders

2018· article· en· W2904664036 on OpenAlexafffund
Scott B. Patten

Bibliographic record

VenueBMC Psychiatry · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of CalgaryAlberta Children's Hospital
FundersUniversity of Calgary
KeywordsIllusionPsychologyStressorMajor depressive disorderDepression (economics)Perspective (graphical)Clinical psychologyCognitive psychologyPsychiatryCognitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Depression often occurs in association with stressful events. However, people with depressive disorders may experience episodes in response to minor stressors or "out of the blue." Similar episodes can occur in people who do not have a disorder in response to severe events. This plurality of symptom patterns, occurring as it does in the absence of precise demarcation from normality has led to controversy over how depressive disorders should be defined, how common they are, and when treatment should be offered. Much of the controversy, however, may be illusory, arising from a tendency to view depressive disorders as defects or disease processes (the "clincian's illusion"). Avoiding the illusion involves understanding depression as a defense rather than a defect and requires consideration of aspects of signal detection theory and the associated "smoke detector" principle. This perspective may help to understand aspects of depressive disorders that are otherwise puzzling and controversial. METHODS: In this paper, implications of signal detection theory and the "smoke detector principle" are explored: (1) conceptually, (2) using calculations performed in a spreadsheet and (3) using an agent-based model. Depressive episodes are conceptualized or represented as all-or-nothing phenomena activated in response to stressful life events. These events occur in an environment that also includes variable levels of baseline stress, creating a signal detection problem. The agent-based framework allows interaction with the environment as agents attempt to achieve an ideal level of adaptation. RESULTS: The smoke detector principle, if valid, may explain otherwise puzzling and controversial features of the depressive disorders, such as their lack of precise demarcation from normality, the role of life events and stressors and their patterns of prevalence. CONCLUSIONS: Signal detection concepts help to avoid the "clinician's illusion" in which aspects of functioning of the body's defenses are mistaken for a disease entity or defect. These principles emphasize inevitable difficulties that are encountered in attempts to conceptualize depressive disorders without reference to the environment in which they occur, and without addressing possible stochastic (randomly varying) elements. Because of the "clinicians illusion", current research priorities, as well as diagnosis and treatment strategies, may be flawed.

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)0.000
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 score0.080
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.102
GPT teacher head0.455
Teacher spread0.354 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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