Bibliographic record
Abstract
Purpose: We aimed to investigate the genetics of antidepressant response in patients with obsessive-compulsive disorder (OCD) in two ethnic groups. Hypothesis: We postulated that different genetic variations across known OCD candidate genes may predict antidepressant response in OCD patients with different ethnic background. Method: We examined two independent and ethnically different OCD samples. The Canadian sample comprises of 222 Caucasian OCD subjects and we investigated 32 single nucleotide polymorphisms (SNPs) across 14 OCD candidate genes and their regulatory regions with antidepressant response data using a custom-made 32-SNP QuantStudio Flex Real-Time PCR System Chip. Individuals were grouped into those who improved following an adequate trial of antidepressant as compared with those who reported "minimal" improvement, "no change", or "worsening" using the Clinical Global Impression -Improvement scale. Pearson 2 test was performed to detect differences in the number of responders versus non-responders across genotype groups. The Brazilian sample consists of 192 Brazilian OCD individuals and 45 SNPs across 18 OCD candidate genes were genotyped. Of the 192 Brazilian OCD participants, 74 completed an adequate antidepressant trial and change of the Yale-Brown Obsessive-Compulsive Scale severity scores pre-and post-treatment were compared between genotype distributions of each examined SNP. Results: For the Canadian sample, interesting associations (P<0.05) were detected for the serotonin genes, HTR2A and HTR1B in antidepressant response. For the Brazilian sample, significant associations were detected for a gabaergic system gene, GABRA3, and antidepressant response (P<0.05). Conclusions: These variants may be clinically useful in predicting treatment resistance versus response in patients with OCD, thereby, reducing their duration of suffering via trial-and-error method of prescribing and improving clinical outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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; both teacher heads agree on what is shown here.
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".