Improving Oral Oncolytic Patient Self-Management
Bibliographic record
Abstract
PURPOSE: Managing patients who are taking oral oncolytics is challenging because of the changing paradigm from frequent supervision during intravenous therapy to periodic observation with oral administration of drugs. We joined the Michigan Oncology Quality Consortium (MOQC) Oral Oncolytics Collaborative in 2013 to identify opportunities for improvement in this area. METHODS: We completed MOQC's baseline self-assessment and performed an audit of medical records for 25 patients prescribed an oral oncolytic from May 2011 to July 2013. We implemented the following MOQC resources: a tracking system for patients taking oral oncolytics, patient education with drug-specific self-care guidelines, use of a modified Edmonton Symptom Assessment Scale, and a medication adherence questionnaire to be used on scheduled follow-up calls and return visits. We modified our workflow to include a standard teaching session and consistent follow-up phone calls. We conducted a retrospective postimplementation medical records audit from August 2013 to September 2014. RESULTS: Baseline self-assessment revealed lack of start date documentation and lack of consistent follow-up. A baseline medical records audit showed that 48% of patients discontinued their medication without consulting their physician, and start date documentation was available for only 52% of patients. After participating in the quality initiative, 100% of patients sampled had a documented start date, and no patients discontinued their drug on their own. Seventeen percent had a dose reduction as a result of toxicity, as directed by the physician. CONCLUSION: The introduction of new office procedures to easily identify all patients receiving oral therapy and improvement in patients' ability to manage symptoms at home with the use of self-care guidelines contributed to an improvement in managing patients who are taking oral oncolytics.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 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".