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Record W7001040598

Illuminating novel predictors of psychosis: Investigations of environmental and bioelectromagnetic predictors of psychosis symptoms in healthy adults

2023· dissertation· en· W7001040598 on OpenAlexaboutno aff

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2023
Typedissertation
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsnot available
Fundersnot available
KeywordsPsychosisSchizophrenia (object-oriented programming)Depression (economics)Schizoaffective disorderBipolar disorderPersistence (discontinuity)Disease
DOInot available

Abstract

fetched live from OpenAlex

Schizophrenia is a debilitating disorder, which often results in irreversible tissue loss in the brain, making it a difficult disorder to treat. The defining feature of schizophrenia is psychosis, which also occurs in schizoaffective disorder, substance use disorders, bipolar disorder, delusional disorder, and dementia. We are slowly getting a better understanding of schizophrenia as novel biomarkers are discovered and we learn what influences its prevalence rates. For example, many studies have shown that schizophrenia is positively correlated with latitude. This knowledge compliments our understanding of the importance of inflammation and vitamin D deficiency as risks for schizophrenia. The purpose of the current thesis was three-fold: first, to determine seasonal variability of background photons as a novel environmental variable to use as a psychosis predictor. Second, to determine if the relationship with latitude was present with psychosis symptoms in healthy adults. And third, to investigate a novel biomarker, biophotons, as a predictor of psychosis/schizotypy symptoms in healthy adults. There were three different studies completed to investigate these questions. The first measured background photon over the course of a year to understand seasonal variations and correlations with other geophysical variables. In the second study, online psychological questionnaires were administered to a global sample. The results suggested the symptoms of psychosis were negatively correlated with latitude, opposite of the previous findings with schizophrenia. Negative correlations were present in spirituality and hypomanic scores, but not depression or anxiety. Additionally, regression analysis revealed that in females but not males, components of the Earth’s electromagnetic field were better at predicting psychosis symptoms. In the third study, biophoton emissions from the hands (BPEs), quantitative electroencephalographic (QEEG), electrocardiographic (ECG), and psychological questionnaires were measured from participants in Sudbury, ON, Canada. The psychological questionnaires used were the Millon Clinical Multiaxial Inventory (MCMI-IV) and the Temperament Character Inventory (TCI-R). The results suggested that biophotons showed some specificity, with overall BPEs from the hands predictive of affective scales in females, and the absolute difference between hands predictive of Schizotypal, Paranoid, and Schizophrenic Spectrum scores in females. Surprisingly, there were very few significant correlations in males. We also found that BPE and QEEG variables combined were able to predict scores on a Depression/Somatic Symptom factor. These results demonstrate that biophotons could be a potential biomarker for mental health disturbances. Taken together, these results demonstrate the importance of investigating the environmental electromagnetic and bioelectromagnetic variables to predict and understand psychosis.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.260
Teacher spread0.241 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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