Use of antidepressants with pharmacogenetic prescribing guidelines in a 10-year depression cohort of adult primary care patients
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To describe the usage patterns of antidepressants with published CYP2D6- and CYP2C19-based prescribing guidelines among depressed primary care patients and estimate the proportion of patients taking antidepressants not recommended for them based on their CYP2C19 and CYP2D6 genotype-predicted metabolizer status. METHODS: Medication use and pharmacogenetic testing results were collected on 128 primary care patients enrolled in a 10-year depression cohort study. At each 12-month interval, we calculated the proportion of patients that: (1) reported use of one or more of the 13 antidepressant medications (i.e. amitriptyline, citalopram, escitalopram, clomipramine, desipramine, doxepin, fluvoxamine, imipramine, nortriptyline, paroxetine, sertraline, trimipramine, venlafaxine) with published CYP2D6- and CYP2C19-based prescribing guidelines, (2) were taking an antidepressant that was not recommended for them based on their CYP2C19 and CYP2D6 genotype-predicted metabolizer phenotype, and (3) switched medications from the previous 12-month interval. RESULTS: The annual proportion of individuals taking an antidepressant with a CYP2D6- and CYP2C19-based prescribing guidelines ranged from 45 to 84%. The proportion of participants that used an antidepressant that was not recommended for them, based on available CYP2D6 and CYP2C19 metabolizer phenotype, ranged from 18 to 29% and these individuals tended to switch medications more frequently (10%) compared to their counterparts taking medication aligned with their metabolizer phenotype (6%). CONCLUSION: One-quarter of primary care patients used an antidepressant that was not recommended for them based on CYP2D6- and CYP2C19-based prescribing guidelines and switching medications tended to be more common in this group. Studies to determine the impact of CYP2D6 and CYP2C19 genotyping on reducing gene-antidepressant mismatches are warranted.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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