Genetic predictors of response to treatment with citalopram in depression secondary to traumatic brain injury
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: To determine which serotonergic system-related single nucleotide polymorphisms (SNPs) predicted variation in treatment response to citalopram in depression following a traumatic brain injury (TBI). METHODS: Ninety (50 M/40 F, aged 39.9, SD = 18.0 years) post-TBI patients with a major depressive episode (MDE) were recruited into a 6-week open-label study of citalopram (20 mg/day). Six functional SNPs in genes related to the serotonergic system were examined: serotonin transporter (5HTTLPR including rs25531), 5HT1A C-(1019)G and 5HT2A T-(102)C, methylene tetrahydrofolate reductase (MTHFR) C-(677)T, brain-derived neurotrophic factor (BDNF) val66met and tryptophan hydroxylase-2 (TPH2) G-(703)T. Regression analyses were performed using the six SNPs as independent variables: Model 1 with response (percentage Hamilton Depression (HAMD) change from baseline to endpoint) as the dependent variable and Model 2 with adverse event index as the dependent variable (Bonferroni corrected p-value < 0.025). RESULTS: MTHFR and BDNF SNPs predicted greater treatment response (R(2)= 0.098, F = 4.65, p = 0.013). The 5HTTLPR predicted greater occurrence of adverse events (R(2)= 0.069, F = 5.72, p = 0.020). CONCLUSION: Results suggest that polymorphisms in genes related to the serotonergic system may help predict short-term response to citalopram and tolerability to the medication in patients with MDE following a TBI.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it