Bibliographic record
Abstract
The purpose of this review is to summarize the current state of knowledge on how nongenetic factors occurring before, during, or soon after birth are related to schizophrenia. Schizophrenia is a complex psychiatric illness with a varied clinical presentation that has both environmental and genetic origins and that may result from insults to the nervous system that occur throughout development. In line with this, several endogenous (internal) and exogenous (external) nongenetic factors of pregnancy and birth have been related to an increased risk for schizophrenia in later life. These factors include maternal diabetes, low birth weight, older paternal age, winter birth, and prenatal maternal stress, among others. Although each of these nongenetic factors alone slightly increases the risk for schizophrenia, risk increases when these factors combine with each other and with other risk factors. The mechanisms that link each specific risk factor with the occurrence of schizophrenia remain largely unknown. In order to build better models of the illness, researchers will have to address the question of how environmental and genetic risk factors work together in increasing risk and explore to what extent certain underlying risk factors may explain different aspects of the disease.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".