Abstract P3-12-11: Disparities in adjuvant hormone adherence in breast cancer patients within a universal healthcare model
Bibliographic record
Abstract
Abstract Background: Patient adherence to adjuvant hormonal therapy for breast cancer (BC) is correlated with improved survival. Recent publications have demonstrated ethnic disparities in adjuvant hormone adherence (AHA) for privatized healthcare models. Objective: To identify disparities in AHA for BC patients within a universal healthcare system in Alberta, Canada. Methods: Patients diagnosed from 2007-2014 with stage I-III, ER+/HER2- BC receiving adjuvant FECD or DC chemotherapy and at least one month of adjuvant hormonal therapy in Alberta, Canada were retrospectively assessed. Hormone monotherapy (tamoxifen, AI), switch strategies (tam to AI), and treatment duration were collected. Compliance was assessed with central pharmacy data. Patient ethnicity was identified using patient first/last and parental last name via Onolytics® ethnographic software. Ethnicity was further verified using a centrally collected place of birth. Age, AJCC stage, psychiatric diagnoses (mood, bipolar), and comorbidity were collected. Log rank and Chi squared were used to assess difference between adjuvant hormonal therapy for variables at 1, 2, and 5 years. Log rank p-values at 2 years are reported. Results: A total of 2,399 ER+ patients were included for analysis. AHA was non-significant for ethnicity (p=0.797), comorbidity (p=0.623), psychiatric disorders (p=0.145), or elderly cohorts (p= 0.814). AHA by stage was significant with stage III > II > I (p=0.004) having the highest compliance rates. AHA was highest for planned hormonal switch strategies (p=0.004) compared to monotherapy. Hormone Adherence RatesCharacteristicsNo. of Patients% AdherenceEthnicity Caucasian211891.6Asian15293.4Middle Eastern/African9593.7Hispanic2491.7Age <65207791.8>6534192.1Comorbidity 096191.21-3134392.2>311493Psychiatric Dx Yes31189.7No210792.1Stage I45388.3II151292.1III45394.5Hormone Strategy Monotherapy173190.9Switch68794.2 Conclusion: AHA is not dependent on ethnicity, age, comorbidity, or psychiatric diagnosis in a universal healthcare model. Conversely, higher rates of AHA are seen with planned switch strategy compared to monotherapy, contradictory to the BIG I-98 trial. Patients with higher stage, and thus higher risk of BC recurrence have increased adherence compared to their low risk counterparts. Reinforcement of AHA for low/moderate risk BC patients, in addition to tamoxifen to AI switch strategies may improve overall adherence. Citation Format: Veitch ZW, Khan OF, Tilley D, Kostaras X, King K, Lupichuk S, Tang P. Disparities in adjuvant hormone adherence in breast cancer patients within a universal healthcare model [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-12-11.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".