SU8. Prevention Research in Major Psychoses Should Draw Concepts From Other Complex Disorders
Bibliographic record
Abstract
Background: Today’s medicine suffers from silos between major psychiatric disorders (MP: schizophrenia, bipolar disorder, recurrent major depression) and other severe complex disorders (eg, cardiovascular, cancer, diabetes). Not only can different complex disorders affect the same patient but they partially share their genetic, environmental, and developmental roots.1–3 Realizing these facts has high clinical and research significance. We compare here the common genetic and biological grounds between complex disorders, the methodological framework guiding clinical surveillance of at-risk children in other fields of medicine and how we can draw from them to inspire future clinical trials in MP prevention. Methods: We reviewed the approaches and clinical guidelines for prevention and early detection of cardiovascular disorders,4 diabetes,5 and asthma6 in children at risk. We could identify common core elements among existing guidelines. We then reviewed the literature on the childhood determinants of MP. Results: The literature showed that all these complex disorders share concepts and methods in their respective risk factors such as familial history, risk endophenotypes, subclinical traits or symptoms, and, later in the risk trajectory, help-seeking behaviors. Complex disorders also share the observation of progressive clustering of risk indicators in young individuals. In contrast to other disorders, no international clinical guidance exists for MP even though risk factors have been well identified, eg, cognitive deficits, electrophysiological anomalies or psychotic-like experiences in childhood. Conclusion: The roots of many complex diseases including MP can be dug up from childhood. In most complex disorders except psychiatry, risk indicators and their aggregation along the risk trajectory have provided a basis for practice guidelines that have outlined evaluation requirements, types of surveillance and even early intervention in lifestyle, nutrition, remediation, or even medical treatment. For MP, even when the accumulated evidence is not sufficient to drive specific preventive treatments, it is at least known how to identify the children most at risk. Realizing these empirical facts may help professionals and scientists to initiate a dialogue with academic authorities to foster clinical guidelines in psychiatry. References 1.Shonkoff JP et al. JAMA. 2009. 2.Maziade M, Paccalet T. Schizophr Res. 2013. 3.Maziade M, Paccalet T. JAMA Pediatr. 2014. 4.Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents. Pediatrics. 2011. 5.Canadian Diabetes Association. Can J Diabetes. 2013. 6.Ducharme FM et al. Can Respir J. 2015.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".