High Rates of Genetic Diagnosis in Psychiatric Patients with and without Neurodevelopmental Disorders: Toward Improved Genetic Diagnosis in Psychiatric Populations
Bibliographic record
Abstract
Objective:There is a paucity of literature on genetic diagnosis in psychiatric populations, particularly the vulnerable population of patients with concomitant neurodevelopmental disorder (NDD). In this cross-sectional study, we investigated the genetic diagnostic rate in 151 adult psychiatric patients from two centers in Ontario, Canada, including a large subset (73.5%) with concurrent NDD, and performed phenotypic analysis to determine the strongest predictors for the presence of a genetic diagnosis.Method:Patients 16 years of age or older and affected with a psychiatric disorder plus at least one of NDD, neurological disorder, congenital anomaly, dysmorphic features, or family history of NDD were recruited through the genetics clinics between 2012 and 2016. Patients underwent genetic assessment and testing according to clinical standards. Chi-squared test was used for phenotypic comparisons. Multivariate logistic regression analysis was performed to determine which phenotypic features were predictive of genetic diagnosis types.Results:Overall, 45.7% of patients in the total cohort were diagnosed with genetic disorders with the vast majority of diagnoses (89.9%) comprising single gene and chromosomal disorders. There were management and treatment implications for almost two-thirds (63.8%) of diagnosed patients. Presence of a single gene disorder or chromosomal diagnosis was predicted by differing combinations of neurological, NDD, and psychiatric phenotypes.Conclusion:The results of this study highlight the frequency and impact of genetic diagnosis in psychiatric populations, particularly those with concomitant NDD. Genetic assessment should be considered in psychiatric patients, particularly those with multiple brain phenotypes (psychiatric, neurodevelopmental, neurological).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".