Oral oncolytic monitoring pilot with patient-reported outcomes and adherence assessments
Bibliographic record
Abstract
IntroductionPatients on oral oncolytics are responsible for self-monitoring adherence and adverse drug reactions (ADRs). Oncology pharmacists are in position to focus on quality and safety of care for patients on oncolytics while providing communication between the patient, physician, and specialty pharmacies. This pilot aimed to monitor patients treated by our leukemia team initiated on oral oncolytics.MethodsFrom July 2020 to February 2021, patients treated by our leukemia team newly started on oncolytics were included. Pharmacists performed medication reconciliation and drug interaction screening on initiation of oral oncolytic. Pharmacists followed up at predefined intervals. On follow up adherence was assessed using the Morisky Medication Adherence Scale-8 (MMAS-8) and patient reported outcomes (PROs) were assessed using the revised Edmonton Symptom Assessment Scale (ESAS-r). After each follow-up, a note was placed in the chart with assessment scores and recommendations.ResultsA total of 32 patients were screened with 19 patients included. Oral oncolytics included: imatinib (4), dasatinib (5), ponatinib (1), gilteritinib (2), enasidenib (1), and venetoclax (6). Fourteen drug interactions were identified, 11 medications discontinued, nine medications added, and two medications doses were changed. Twenty-six adherence assessments were performed with 21, 4, and 1 assessment demonstrating adherence, medium adherence, and low adherence, respectively. 62 ESAS-r assessments were performed with 64% reported as no symptoms, 17% as mild, 13% as moderate, and 5% as severe symptoms. Twenty laboratory tests were ordered from pharmacist recommendation on initiation and follow-up.ConclusionThis pilot demonstrated the role pharmacists play in oral oncolytic monitoring and symptom management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".