Bibliographic record
Abstract
Introduction Schizophrenia is a psychiatric disorder with multiple causes, including genetic, immune, environmental causes of various kinds, which all increase the vulnerability and predisposition to the disorder. Among them stand out prenatal infections, thus being a preventable risk potential factor in our daily clinical practice. Objectives To analyze the relation between prenatal infections and schizophrenia. Methods Review of the subject and recent articles on schizophrenia in psychiatric guides and magazines. Results After analyzing several studies, it have shown that prenatal infections, where the nervous system is not yet fully developed, may be a risk factor for the development of schizophrenia in adults, in relation with genetically predisposed individuals. Infections such as influenza, especially during the first quarter of gestation; rubella, toxoplasma and herpes simplex virus-type 2 are related to potentially increase risk of suffer schizophrenia. Conclusions Prenatal infections, especially in the first quarter and the periconceptional period, constitute a risk factor in individuals with vulnerability to develop schizophrenia. Awareness and prevention is important in the pregnant population of the influence of these infections on the possible origin of psychotic episodes. Disclosure of interest The authors have not supplied their declaration of competing interest.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".