Interacting with drugs used in oncology: an Alberta Cancer Board initiative
Bibliographic record
Abstract
Objective. The Alberta Cancer Board (ACB) Pharmacy recognized the importance of having a sound knowledge of drug interactions with anti-neoplastic agents. A pharmacy student was given the task of surveying ACB pharmacists for published literature and Internet sources used most often as sources of drug interaction information during medication order review and evaluation of patient profiles. The goal was to extract information from these sources and integrate it into a concise ACB drug interaction database, which lists the actual and potential antineoplastic drug-drug, drug-disease, drug-natural products and drug-food interactions. The antineoplastic agents identified and examined were part of the ACB Out-patient Cancer Drug Benefit Program (OCDBP) (August 2002). Equipped with this information and using their clinical expertise, pharmacists can play an important role in the medical management of patients with cancer. Data sources. The student collected information from literary and remote sources used most often by ACB pharmacists. Study selection. ACB pharmacists were surveyed for published literature and Internet sources that contained drug interaction information on antineoplastics used most often during medication order review, patient counselling sessions and when responding to drug information requests. The contributors of these information sources claim to have reviewed existing references and thus the student relied on the accurate reporting of their findings instead of collecting data from the original works. The drugs reviewed for the purpose of the study were taken from the ACB OCDBP. Data extraction. There was an extensive data collection process. Dorland’s definition of a drug interaction was adopted. The student determined a potential interaction based on the words `may’ or `risk’ found in the statement of the interaction. The student extracted the drug interaction information into tables and a sample of 10 of 87 drugs from the ACB OCDBP are presented in this paper. A complete guide of the ACB drug interaction index can be obtained from the ACB Pharmacy. Data synthesis. Actual and potential antineo-plastic drug-drug, drug-disease, drug-natural products and drug-food interactions are summarized in tables. The information collected was as current as the date that the data were extracted. The determination of clinical significance relies on the expertise of the pharmacist or other healthcare professional. Moreover, the data contained in the tables is representative of the information collected exclusively from sources used most often by ACB pharmacists and therefore is only one source of drug interaction information. Conclusion. An indepth knowledge of actual and potential interactions with antineoplastic agents is critical for pharmacists in the management of patients with cancer. The ACB drug interaction index is an attempt to promote awareness of interactions with antineoplastic drugs and to encourage clinicians to investigate drug interactions during their treatment and management protocols.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".