Group medical appointments: A novel approach in patient education for adjuvant endocrine therapy.
Bibliographic record
Abstract
133 Background: Group medical appointments (GMA) are currently practiced for a wide range of medical conditions such as diabetes, hypertension, asthma, and cardiovascular disease. Previously, postmenopausal estrogen receptor positive breast cancer patients (ERBCP) attended individual physician clinic appointments to learn about their options for adjuvant endocrine therapy. This resulted in variable education provided, lengthy medical oncologist (MO) clinic visits and significant wait-lists to attend clinic. Accordingly, we embarked on a pilot program to determine the feasibility and acceptability of GMA in this patient population. Methods: Since 2010, ERBCP requiring endocrine therapy were referred and scheduled in the biweekly GMA program. Education regarding choices, risks, benefits and side effects of endocrine therapy were provided by a nurse practitioner (NP) and/or pharmacist (RX). After questions were solicited from the group, individual ERBCP were provided with prescriptions and scheduled for guideline-based follow-up. Results: Approximately 900 ERBCP have attended GMA, with 100% of MOs referring eligible patients. Surveys indicate high levels of satisfaction with the information provided and the GMA format. Conclusions: GMA provided by NP and/or RX is feasible and acceptable to both ERBCP and MOs. Health system benefits include increased efficiency and reduced cost, with MO clinic reserved for complex patient needs. Patient benefits include timely access to care and high levels of reported satisfaction. Future work will examine the effects of GMA on patient compliance and medication reconciliation with endocrine therapy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".