Gene-based drug therapy for children and youth treated with psychoactive medications
Bibliographic record
Abstract
Abstract Psychoactive medications are increasingly used to treat children and youth with mental health conditions, but individual variations in response highlight the need for precision medicine. Pharmacogenetic (PGx) testing is a key component of precision medicine. The number of commercial pharmacogenetic testing companies promoting PGx, with the promise of achieving individualized and effective treatment of mental health conditions, has grown exponentially in recent years. Scientific evidence supporting the use of PGx to manage mental health conditions is limited, especially for paediatric populations. This practice point outlines steps guiding the use and interpretation of PGx testing for psychoactive medications in clinical settings, along with key supportive resources. Practice guidelines have been developed for variants in pharmacogenes encoding cytochrome P450 drug-metabolizing enzymes (e.g., CYP2C19, CYP2D6, CYP2C9) as one determinant of drug concentrations in blood, which can support both drug choice and dosing strategy for certain anti-psychotics, anti-depressants, and anti-epileptics. Adverse drug reactions to some anti-epileptic drugs (e.g., carbamazepine and phenytoin) have been associated with certain human leukocyte antigen types and variants in DNA polymerase gamma (POLG; valproic acid). Evidence remains limited for genetic variants of drug target proteins, making it challenging to identify patients with altered treatment responses at a therapeutic blood concentration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".