Primary non-adherence to adjuvant endocrine therapy in older women with breast cancer.
Bibliographic record
Abstract
285 Background: Adjuvant endocrine therapy (AET) improves survival in hormone receptor positive breast cancer (HR+BC). Challenges with adherence to AET in seniors are well documented; however, there is limited knowledge on primary non-adherence (PNAD). PNAD is defined as non-initiation of a prescribed medication. Our aim is to characterize PNAD rates in women aged ≥ 65 with HR+BC and identify potential predictors, using real-time treatment information. Methods: Optimum is an e-health platform integrating real-time analysis of administrative claims data combined to patient-level clinical information on breast cancer. Optimum tracks care trajectories to identify deviations from best practice, using data from Quebec’s universal health insurance plan that covers all medical and pharmaceutical care. In this single-center feasibility study, we characterized PNAD as a non-initiation of AET within 10 days from the first prescription. Descriptive analyses were used to assess potential predictors. Results: Of the 57 patients enrolled, 9 were excluded due to lack of > 30 day follow up. In the remaining 48 patients, PNAD was 21 %. Baseline Charlson comorbidity index (0 vs 13 %), psychotropic drug use (20 % vs 26 %) and polypharmacy rate (10 % vs 11 %) were lower in PNAD patients, compared to primary-adherent patients. PNAD patients had larger average tumor size (1.8 cm vs 1.6 cm), more often overexpressing HER2NEU (10 % vs 3 %), more negative progesterone receptor (10 % vs 5 %). They also more often had lumpectomy (70 % vs 65 %), SLNB (70 % vs 58 %) and more frequent margin revisions (30 % vs 16 %). They more often received chemotherapy (30 % vs 0 %). At 30-day follow-up, 40 % of PNAD patients had not yet initiated AET. Conclusions: This study confirms the feasibility of combining real-time administrative data and patient-level clinical information to assess breast cancer quality care. PNAD in women with HR+BC was higher than expected. PNAD patients had less comorbidities and drug use, but more aggressive cancers and more often also had quality challenges with surgical care (margin revision). PNAD predictors can potentially be used to identify patients that may require additional support to optimize disease management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".