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Record W2912510292 · doi:10.1136/ebmental-2018-300076

Shedding light on the onset of psychiatric illness: looking through a developmental lens

2019· letter· en· W2912510292 on OpenAlexafffund
Sarah Goodday, Anne Duffy

Bibliographic record

VenueEvidence-Based Mental Health · 2019
Typeletter
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsQueen's University
FundersInstitute of Neurosciences, Mental Health and AddictionWellcome Trust
KeywordsPsychopathologyPsychiatryBipolar disorderSchizophrenia (object-oriented programming)PsychologyAnxietyMental illnessDepression (economics)ManiaDevelopmental psychopathologyLongitudinal studyClinical psychologyMedicineMental healthCognition

Abstract

fetched live from OpenAlex

Understanding the mechanisms driving the onset of major psychiatric illnesses such as depression, bipolar disorder and schizophrenia has been far more challenging than expected. Contributing factors include that symptoms associated with each of these disorders overlap during different phases of emerging illness1 and over the course of established illness.2 In addition, while there is evidence supporting specificity of familial segregation,3 at least some genetic risk factors appear to be shared4 and treatment response crosses currently defined diagnostic boundaries.5  But does it follow, as some propose,6 that psychiatric illnesses are really different manifestations of a common origin—in other words that different psychiatric illnesses derive from pluripotent or shared beginnings and specific outcomes depend on mediating and moderating influences? Epidemiological and high-risk prospective studies provide compelling evidence to the contrary. In fact, a developmental perspective has illuminated clear distinctions in the early trajectories between illness such as schizophrenia and bipolar disorder.7 Further longitudinal studies of children over the peak risk period have highlighted the inadequacy of current cross-sectional approaches to psychiatric diagnosis that do not take into account the developmental and emergent course of psychopathology against the backdrop of family history and other predictive risk factors.8 Using bipolar disorder as an example, longitudinal studies of high-risk offspring of affected parents followed through childhood into adulthood have clearly demonstrated that psychopathology is in evolution—a moving target. What first manifests as anxiety or sleep problems in high-risk children might evolve into a depressive disorder in adolescence followed by an index hypomanic or manic episode in emerging adulthood.9 However, this evolving developmental trajectory differs between distinct end-stage illnesses. For example, clinical and neuroimaging evidence supports that the trajectory into schizophrenia is consistent with a neurodevelopmental process,10 which is distinctively different from the prototypical developmental trajectory …

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.250
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.043
GPT teacher head0.318
Teacher spread0.275 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2019
Admission routes2
Has abstractyes

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