Experience with Lexicomp® Online Drug Database for Medication Review and Drug-Drug Interaction Analysis within a Comprehensive Geriatric Assessment in Elderly Cancer Patients
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it