Resilience Factors Preventing Schizophrenia in Ultra-high Risk Patients: Lessons from Genetics
Bibliographic record
Abstract
Over the past decades, researchers and psychiatrists in the field of psychosis have moved from a conception of a chronic presentation to a more dynamic paradigm. Accordingly, schizophrenia is now conceptualized as a progressive illness that typically emerges during late adolescence and follows different stages: early vulnerability, ultra-high risk state, first episode of psychosis, and chronic disease. Only one-quarter of the ultra-high risk patients will convert to a full-blown psychotic episode within 3 years while the others, called non-converters, will remain at-risk, develop other psychiatric disorders, or fully recover. The reasons for this differential outcome are not yet understood but this concept opens the way to scientific research to determine the protective factors involved in resilience for non-converters. Based on the Gene X Environment interaction model, schizophrenia results from genetic vulnerability and environmental aggressions which can have an impact on the epigenome and gene expression. Recent studies have shown that genetic variants play a role in the resilience of psychosis. Polygenic risk scores, computed as the addition of genetic polymorphisms, can modulate the effects of genetic at-risk deletions (i.e. del22q11) that predispose to psychosis and may also influence the cognitive symptoms of ultra-high risk patients. Resilience, defined as the ability to withstand adversity, is not only related to external skills or psychotherapeutic care but could also be explained by internal molecular factors. Identifying the genetic factors of resilience might help to stratify the risk and to develop precision medicine in psychiatry. Disclosure No significant relationships.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".