Illuminating novel predictors of psychosis: Investigations of environmental and bioelectromagnetic predictors of psychosis symptoms in healthy adults
Bibliographic record
Abstract
Schizophrenia is a debilitating disorder, which often results in irreversible tissue loss in the \nbrain, making it a difficult disorder to treat. The defining feature of schizophrenia is psychosis, \nwhich also occurs in schizoaffective disorder, substance use disorders, bipolar disorder, delusional \ndisorder, and dementia. We are slowly getting a better understanding of schizophrenia as novel \nbiomarkers are discovered and we learn what influences its prevalence rates. For example, many \nstudies have shown that schizophrenia is positively correlated with latitude. This knowledge \ncompliments our understanding of the importance of inflammation and vitamin D deficiency as \nrisks for schizophrenia. The purpose of the current thesis was three-fold: first, to determine \nseasonal variability of background photons as a novel environmental variable to use as a psychosis \npredictor. Second, to determine if the relationship with latitude was present with psychosis \nsymptoms in healthy adults. And third, to investigate a novel biomarker, biophotons, as a predictor \nof psychosis/schizotypy symptoms in healthy adults. There were three different studies completed \nto investigate these questions. The first measured background photon over the course of a year to \nunderstand seasonal variations and correlations with other geophysical variables. In the second \nstudy, online psychological questionnaires were administered to a global sample. The results \nsuggested the symptoms of psychosis were negatively correlated with latitude, opposite of the \nprevious findings with schizophrenia. Negative correlations were present in spirituality and \nhypomanic scores, but not depression or anxiety. Additionally, regression analysis revealed that in \nfemales but not males, components of the Earth’s electromagnetic field were better at predicting \npsychosis symptoms. In the third study, biophoton emissions from the hands (BPEs), quantitative \nelectroencephalographic (QEEG), electrocardiographic (ECG), and psychological questionnaires \nwere measured from participants in Sudbury, ON, Canada. The psychological questionnaires used were the Millon Clinical Multiaxial Inventory (MCMI-IV) and the Temperament Character \nInventory (TCI-R). The results suggested that biophotons showed some specificity, with overall \nBPEs from the hands predictive of affective scales in females, and the absolute difference between \nhands predictive of Schizotypal, Paranoid, and Schizophrenic Spectrum scores in females. \nSurprisingly, there were very few significant correlations in males. We also found that BPE and \nQEEG variables combined were able to predict scores on a Depression/Somatic Symptom factor. \nThese results demonstrate that biophotons could be a potential biomarker for mental health \ndisturbances. Taken together, these results demonstrate the importance of investigating the \nenvironmental electromagnetic and bioelectromagnetic variables to predict and understand \npsychosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".