Understanding putative risk factors for schizophrenia: retrospective and prospective studies
Bibliographic record
Abstract
This paper describes a research program intended to provide a better understanding of the influence of several putative risk factors for schizophrenia on child development and psychosis. Two related components of the overall program are described: the retrospective EnviroGen projects, which use a variety of putative risk factors to explain variance in several dimensions of schizophrenia and in psychotic symptoms in community controls, and Project Ice Storm, which prospectively examines the effects of prenatal maternal stress in the children of women who were exposed to the 1998 Quebec ice storm during their pregnancies. The EnviroGen projects have been successful in explaining variance in several dimensions of illness, including premorbid adjustment and severity of dissociative symptoms. Project Ice Storm has demonstrated the noxious effects of prenatal stress on cognitive and language development in children. We have also found that "ice storm children" exposed in specific weeks of gestation show greater dermatoglyphic asymmetry, as has been reported for samples of patients with schizophrenia. In both studies, prenatal maternal stress has been associated with more severe childhood behaviour problems. The combination of retrospective and prospective studies is a rich source of triangulated results providing information about developmental psychopathology.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".