Effects of Pharmacogenomic Testing in Clinical Pain Management: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The availability of pharmacogenomic (PGx) methods to determine the right drug and dosage for individualized patient treatment has increased over the past decade. Adoption of the resulting PGx reports in a clinical setting and monitoring of clinical outcomes is a challenging and long-term commitment. OBJECTIVE: This study summarizes an extended PGx deep sequencing panel intended for medication dosing and prescription guidance newly adopted in a pain management clinic. The primary outcome of this retrospective study reports the number of cases and types of drugs covered, for which PGx data appears to have assisted in optimal drug prescription and dosing. METHODS: A PGx panel is described, encompassing 23 genes and 141 single-nucleotide polymorphisms or indels, combined with PGx dosing guidance and drug-gene interaction (DGI) and drug-drug interaction (DDI) reporting to prevent adverse drug reactions (ADRs). During a 2-year period, patients (N=171) were monitored in a pain management clinic. Urine toxicology, PGx reports, and progress notes were studied retrospectively for changes in prescription regimens before and after the PGx report was made available to the provider. An additional algorithm provided DGIs and DDIs to prevent ADRs. RESULTS: Among patient PGx reports with medication lists provided (n=146), 57.5% (n=84) showed one or more moderate and 5.5% (n=8) at least one serious PGx interaction. A total of 96 (65.8%) patients showed at least one moderate and 15.1% (n=22) one or more serious DGIs or DDIs. A significant number of active changes in prescriptions based on the 102 PGx/DGI/DDI report results provided was observed for 85 (83.3%) patients for which a specific drug was either discontinued or switched within the defined drug classes of the report, or a new drug was added. CONCLUSIONS: Preventative action was observed for all serious interactions, and only moderate interactions were tolerated for the lack of other alternatives. This study demonstrates the application of an extended PGx panel combined with a customized informational report to prevent ADRs and improve patient care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".