Engagement in a statewide oral oncolytic collaborative and practice impact.
Bibliographic record
Abstract
89 Background: The rapid shift to oral oncolytic therapy presents challenges to oncology practitioners. The purpose of this study is to describe how participation in a statewide oral oncolytic improvement collaborative where best practices and resources were shared can readily impact quality of care as measured by national standards. Methods: The Michigan Oncology Quality Consortium (MOQC) hosted a series of learning collaborative sessions focused on topics and deployment of resources specific to oral oncolytic management and quality improvement. Participating practices performed pre/post self-assessments in Oct. 2013 and Apr. 2015 (n = 3). Concordance with national ASCO QOPI and ONS standards was compared, including documentation (5 measures), patient education (7 measures), and follow-up/monitoring (4 measures). A response scale of always, sometimes, and never was used. Specifically, practices were surveyed to assess which evidence-based MOQC resources were implemented: patient intake template, drug-specific self-management guides, start date mailer, medication calendar, primary care physician communication template, Edmonton Symptom Assessment Scale (ESAS), and patient adherence questionnaires. Results: Practice A showed improvement in documentation, patient education, and monitoring. This practice used the initial oral chemotherapy template, ESAS, patient education templates, and patient calendar (Table). Practice B implemented 6 resources and demonstrated improvements in 15 metrics. Practice C implemented 4 resources, namely patient-focused resources, to improve all patient education and monitoring metrics. Conclusions: Use of the collaborative model and supplying oncology teams with scientific evidence, standard workflows, and resources improves concordance with national standards of care. Large-scale deployment of this model program may provide a clinically efficient and effective mechanism to enhance widespread change. [Table: see text]
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".