Gene panel to guide antiseizure medication prescribing: Does the cost justify the benefits?
Bibliographic record
Abstract
Pharmacogenomics hold the potential to identify variants associated with adverse drug reactions and treatment efficacy of anti-seizure medications. A model-based cost-utility analysis by Gordon and colleagues showed that genetically-guided therapy costs more, yielded higher quality-adjusted life years outcomes, and was considered to be cost-effective compared to usual care. The study provided preliminary evidence on the value of pharmacogenetic testing in patients with drug-resistant epilepsy. However, data input for the model was based on assumptions that need to be empirically tested. Further, there are many other factors that may affect the cost-effectiveness of pharmacogenetic testing that need to be considered, including the model of service delivery, its implementation in complex clinical service, whether clinicians will modify treatment decisions based on pharmacogenetic information, and the fidelity with which recommendation on testing is adhered to in the real-world. The cost-effectiveness analysis should be repeated when more robust data on the effectiveness of pharmacogenetics are available and conducted alongside a budget impact analysis, incorporating the direct health care resources required to implement widespread testing and potential subsequent changes in treatment.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".