Understanding Predisposition to Schizophrenia: Toward Intervention and Prevention
Bibliographic record
Abstract
OBJECTIVE: Early intervention to prevent schizophrenia is one of the most important goals of schizophrenia research. However, the field is not yet ready to initiate trials to prevent prodromal or psychotic symptoms in people who are at risk for developing the disorder. In this paper, we consider some of the major obstacles that must be studied before prevention strategies become feasible. METHOD AND RESULTS: One of the most important hurdles is the identification of a syndrome or set of traits that reflects a predisposition to schizophrenia and that might provide potential targets for intervention. In a recent reformulation of Paul Meehl's concept of schizotaxia, we integrate research findings obtained over the last 4 decades to propose a syndrome with meaningful clinical manifestations. We review the conceptualization of this syndrome and consider its multidimensional clinical expression. We then describe preliminary research diagnostic criteria for use in adult, nonpsychotic, first-degree relatives of patients diagnosed with schizophrenia, based on negative symptoms and neuropsychological deficits. We follow this with evidence supporting the validity of the proposed syndrome, which mainly includes social dysfunction and response to a low dosage of one of the newer antipsychotic medications. CONCLUSIONS: Continued progress toward the eventual initiation of prevention strategies for schizophrenia will include sustained efforts to validate the traits reflecting a predisposition to develop the disorder (for example, schizotaxia), follow-up studies to confirm initial findings, and the identification of potentially useful preventive interventions.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".