Assessment of scheduled pharmacy follow-up for patients receiving oral chemotherapy.
Bibliographic record
Abstract
151 Background: Management of oral chemotherapy presents many challenges to oncology practitioners. The purpose of this study is to describe how incorporation of pharmacy scheduled calls to oncology patients receiving oral chemotherapy can identify patients who are experiencing moderate-to-severe symptom burden and/or medication non-adherence and require a therapeutic intervention. Methods: We utilized multiple resources from a statewide quality collaborative aimed at the management of patients receiving oral chemotherapy including patient education, a patient reported outcome (PRO) assessment, and templated documentation. As part of our planned workflow enhancements, our pharmacist provided patient education before chemotherapy started and regularly called patients within 2 weeks of starting their oral therapy. The pharmacist asked the patient about symptoms and adherence using the PRO assessment. A chart audit was performed to summarize the patient reports and recorded interventions. Results: 203 patients taking oral chemotherapy were called by the pharmacist in an 18-month time period between 10/1/16 - 3/28/18. In this study, we will summarize patient demographic information including age, gender, diagnosis, oral chemotherapy medication; PRO assessment results including symptom burden based on the Edmonton Symptom Assessment Scale (ESAS), most bothersome symptom, confidence to self-manage, medication adherence, and reasons for non-adherence, and pharmacist interventions and consequential treatment modifications. Conclusions: Patients taking oral chemotherapy for the treatment of cancer are at risk of side effects from their treatment and suboptimal therapeutic benefits in the setting of non-adherence. Our analysis will summarize the degree of symptom burden patients experience in the first couple weeks of treatment and areas were a pharmacist may optimize their medication regimen prior to their scheduled follow-up with their oncologist. We plan to use this data to improve patient self-management support.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".