Whether ionizing radiation is a risk factor for schizophrenia spectrum disorders?
Bibliographic record
Abstract
The neural diathesis-stressor hypothesis of schizophrenia, where neurobiological genetic predisposition to schizophrenia can be provoked by environmental stressors is considered as a model of the effects of exposure to ionizing radiation. Analysis of information from electronic databases (MEDLINE, PsycINFO, EMBASE, Current Contents, Elsevier BIOBASE) and hand-made search was carried out. There are comparable reports on increases in schizophrenia spectrum disorders following exposure to ionizing radiation as a result of atomic bombing, nuclear weapons testing, the Chernobyl accident, environmental contamination by radioactive waste, radiotherapy, and also in areas with high natural radioactive background. The results of experimental radioneurobiological studies support the hypothesis of schizophrenia as a neurodegenerative disease. Exposure to ionizing radiation causes brain damage with limbic (cortical-limbic) system dysfunction and impairment of informative processes at the molecular level that can trigger schizophrenia in predisposed individuals or cause schizophrenia-like disorders. It is supposed that ionizing radiation can be proposed as a risk factor for schizophrenia spectrum disorders. The hypothesis that ionizing radiation is a risk factor for schizophrenia spectrum disorders can be tested using data from the Chernobyl accident aftermath. Implementation of a study on schizophrenia spectrum disorders in Chernobyl accident victims is of significance for both clinical medicine and neuroscience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".