Bibliographic record
Abstract
INTRODUCTION: Advances in genotyping, mapping and genome analysis methods over the last few years offer great promise towards the discovery of genes involved in the pathogenesis of schizophrenia, a mental disorder with a high degree of heritability. METHODS: This article draws on published reports in major international journals in the field of schizophrenia and human genetics. RESULTS: We summarise the major findings from family linkage studies, genome scan with microsatellite markers and association studies with polymorphisms in candidate genes. However, although recent developments in the technology for genotyping and gene identification have provided new leads to genetic abnormalities underlying schizophrenia, they have yet to result in the identification of any disease gene. There are both positive and negative data for reported linkages to specific chromosomal regions and candidate gene polymorphisms. CONCLUSIONS: Conflicting data and nonreplication of association and linkage studies are problems that need to be addressed. One solution might be clearer and narrower definition of subphenotypes and use of only a specific phenotypic marker in linkage and association studies. When successful, identification of susceptibility genes will lead to better understanding of cognitive functions and socio-emotional behaviour and also help in the formulation of preventive strategies for those found to be at high risk.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".