Quality Measure Concepts to Fill Gaps in Assessing Oral Oncolytic Adherence: A Multistakeholder Measurement Strategy
Bibliographic record
Abstract
Oral oncolytics affect the way oncology providers and their patients manage treatment. Patients with cancer often prefer oral therapies; however, at-home oral treatments may lead to increased non-adherence. Medication adherence quality measures have been used to monitor adherence for other medication types, but none exist for oral oncolytic therapies. A multistakeholder workgroup of oncology and quality measurement experts explored gaps in quality measures focused on oral oncolytic adherence and identified, prioritized, and refined measure concepts for further development. The workgroup prioritized measure gaps and developed measure concepts to assess priority factors impacting adherence: 1) screening for oral oncolytic medication access challenges and 2) bidirectional patient and provider communication regarding oral oncolytic treatment. They further prioritized development of medication persistence rate and discontinuation rate measures. The workgroup then identified action steps to advance the measure concepts, including evidence generation, agreement on best practices to support adherence, identification and use of patient-reported outcomes measures and tools, and integration of measurement data components into existing workflows. Pursuing these recommendations will require collaboration across stakeholders.
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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 | 0.459 | 0.438 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.006 | 0.015 |
| 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".