A familial risk enriched cohort as a platform for testing early interventions to prevent severe mental illness
Bibliographic record
Abstract
BACKGROUND: Severe mental illness (SMI), including schizophrenia, bipolar disorder and severe depression, is responsible for a substantial proportion of disability in the population. This article describes the aims and design of a research study that takes a novel approach to targeted prevention of SMI. It is based on the rationale that early developmental antecedents to SMI are likely to be more malleable than fully developed mood or psychotic disorders and that low-risk interventions targeting antecedents may reduce the risk of SMI. METHODS/DESIGN: Families Overcoming Risks and Building Opportunities for Well-being (FORBOW) is an accelerated cohort study that includes a large proportion of offspring of parents with SMI and embeds intervention trials in a cohort multiple randomized controlled trial (cmRCT) design. Antecedents are conditions of the individual that are distressing but not severely impairing, predict SMI with moderate-to-large effect sizes and precede the onset of SMI by at least several years. FORBOW focuses on the following antecedents: affective lability, anxiety, psychotic-like experiences, basic symptoms, sleep problems, somatic symptoms, cannabis use and cognitive delay. Enrolment of offspring over a broad age range (0 to 21 years) will allow researchers to draw conclusions on a longer developmental period from a study of shorter duration. Annual assessments cover a full range of psychopathology, cognitive abilities, eligibility criteria for interventions and outcomes. Pre-emptive early interventions (PEI) will include skill training for parents of younger children and courses in emotional well-being skills based on cognitive behavioural therapy for older children and youth. A sample enriched for familial risk of SMI will enhance statistical power for testing the efficacy of PEI. DISCUSSION: FORBOW offers a platform for efficient and unbiased testing of interventions selected according to best available evidence. Since few differences exist between familial and 'sporadic' SMI, the same interventions are likely to be effective in the general population. Comparison of short-term efficacy of PEI on antecedents and the long term efficacy for preventing the onset of SMI will provide an experimental test of the etiological role of antecedents in the development of SMI.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".