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Record W2331451895 · doi:10.1177/0004867416642023

Occipital bending in schizophrenia

2016· article· en· W2331451895 on OpenAlexafffund
Jerome J. Maller, Rodney J. Anderson, Richard H. Thomson, Zafiris J. Daskalakis, Jeffrey V. Rosenfeld, Paul B. Fitzgerald

Bibliographic record

VenueAustralian & New Zealand Journal of Psychiatry · 2016
Typearticle
Languageen
FieldMathematics
TopicPoint processes and geometric inequalities
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersUniversity of TorontoMayo Clinic
KeywordsOccipital lobeSchizophrenia (object-oriented programming)White matterPsychologyPsychosisMedicinePsychiatryAudiologyNeuroscienceMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate the prevalence of occipital bending (an occipital lobe crossing or twisting across the midline) in subjects with schizophrenia and matched healthy controls. METHOD: Occipital bending prevalence was investigated in 37 patients with schizophrenia and 44 healthy controls. RESULTS: Ratings showed that prevalence was nearly three times higher among schizophrenia patients (13/37 [35.1%]) than in control subjects (6/44 [13.6%]). Furthermore, those with schizophrenia had greater normalized gray matter volume but less white matter volume and had larger brain-to-cranial ratio. CONCLUSION: The results suggest that occipital bending is more prevalent among schizophrenia patients than healthy subjects and that schizophrenia patients have different gray matter-white matter proportions. Although the cause and clinical ramifications of occipital bending are unclear, the results infer that occipital bending may be a marker of psychiatric illness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.319
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2016
Admission routes2
Has abstractyes

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Same venueAustralian & New Zealand Journal of PsychiatrySame topicPoint processes and geometric inequalitiesFrench-language works237,207