An Innovative Software Solution for Personalized Pharmacotherapy
Bibliographic record
Abstract
Drug-drug and drug-gene interactions are an important issue in healthcare. In the USA, in 1994-1995, it was estimated that there were approximately 2 million severe adverse drug reactions resulting in 76,000-137,000 deaths. A more recent study published in 2004 shows that 5% of hospital admissions in the UK are directly attributable to adverse drug reactions. Moreover, adverse drug reactions in the USA contribute to significant hospital costs of between US$1.5 and 4 billion per year. Drug metabolism is one determinant of how our bodies respond to drugs. Polymorphisms of the six major cytochrome drug metabolizing genes can lead to either poor metabolism of drugs, hence, increasing probability of toxic reactions, or enhanced metabolism leading to decreased efficacy; with opposite affects for prodrugs. Also, there are the potentially increased costs due to wastage, lack of therapeutic response, repeat doctor visits and poor patient compliance. In addition, when multiple drugs are co-administered some may act as enzyme inducers or enzyme inhibitors further complicating expected drug responses. Considering today's polypharmacy, the number of over-the-counter drugs used, environmental exotoxins, which may inhibit or induce drug metabolism (cigarette smoke), nutrients and other foods, the combination of possibilities of cytochrome P450 interactions and drug-drug interactions affecting a patient response to therapy is overwhelming. A dedicated pharmaceutical decision support software solution, designed to be intuitive, informative and provide ease of use, would greatly increase the probabilities that patients could receive much more individualized treatment. The Rx Factor, through proprietary algorithms, provides the clinician with a dosage modification recommendation for all major substrate medications being prescribed or taken, by an individual.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".