Experience with Lexicomp® Online Drug Database for Medication Review and Drug-Drug Interaction Analysis within a Comprehensive Geriatric Assessment in Elderly Cancer Patients
Bibliographic record
Abstract
Background: We studied the use of Lexicomp®, an online drug information database, for adequate identification of drug-drug interactions (DDIs) within Comprehensive Geriatric Assessment (CGA) in cancer patients. Materials and Methods: Data of 149 onco-geriatric patients were reviewed. Sixty-three percent participated in an observational study recruiting head and neck cancer patients (H&N-group), 37% in a registry recruiting general oncology patients (GO-group). Baseline drug information was collected by a health professional, through the medical interview within CGA. Drug class usage was quantified and potential DDIs were assessed and categorized (risk rating "C": monitor therapy, "D": consider therapy modification, "X": avoid combination) with Lexicomp®. Results: On average, H&N and GO-patients took 5 and 8 prescription drugs at presentation, respectively. An average of 4 drugs were added in both groups as part of their proposed therapy. Potential DDIs (n=211 H&N; n=247 GO) were detected by Lexicomp® in 64.9% (85.3% "C", 14.7% "D", 0% "X") and 83.6% (83.4% "C", 15.8% "D", 0.8% "X") of H&N and GO patients, respectively, at therapy start. Administration of cancer-therapy-related drugs lead to additional DDIs (n=75 H&N; n=68 GO) in 73.7% and 58.3% of H&N and GO cases, respectively. DDIs occurred mainly with supportive drugs (100% H&N and 83.8% GO). Sixteen percent of potential DDIs were identified with anti-neoplastic drugs in the GO-group. In 28.7% and 60.0% of H&N and GO patients, respectively, at least one drug was not recognized by Lexicomp®. Conclusions: Use of Lexicomp® drug database within CGA is feasible. It could reduce the administration of inappropriate drugs, and in that way improve the quality of patient-individualized therapy.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".