Oral Endocrine Therapy Nonadherence, Adverse Effects, Decisional Support, and Decisional Needs in Women With Breast Cancer
Bibliographic record
Abstract
Background Oral endocrine therapy (OET) such as or inhibitors reduces recurrence and mortality for the 75% of breast cancer survivors (BCSs) with a diagnosis of estrogen receptor-positive breast cancer. Because many BCSs decide not take OET as recommended because of adverse effects, understanding BCSs' decisional supports and needs is foundational to supporting quality OET decision making about whether to adhere to OET. Objective The aim of this study was to examine literature pertaining to OET nonadherence and adverse effects using the Ottawa Decision Support Framework categories of decisional supports and decisional needs because these factors potentially influence OET use. Methods A systematic literature search was performed in PubMed and CINAHL using combined search terms aromatase inhibitors and adherence and tamoxifen and adherence. Studies that did not meet criteria were excluded. Relevant data from 25 publications were extracted into tables and reviewed by 2 authors. Results Findings identified the impact of adverse effects on OET nonadherence, an absence of decisional supports provided to or available for BCSs who are experiencing OET adverse effects, and the likelihood of unmet decisional needs related to OET. Conclusions Adverse effects contribute to BCSs decisions to stop OET, yet there has been little investigation of the process through which that occurs. This review serves as a call to action for providers to provide support to BCSs experiencing OET adverse effects and facing decisions related to nonadherence. Implications for practice Findings suggest BCSs prescribed OET have unmet decisional needs, and more decisional supports are needed for BCSs experiencing OET adverse effects.
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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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".