Adherence to adjuvant hormone therapy (AHT) in early breast cancer (BC).
Bibliographic record
Abstract
e11522 Background: Randomized controlled trials of early BC consistently demonstrate a survival benefit from AHT. Some studies show that adherence to AHT is suboptimal, but data are conflicting. There are concerns that poor adherence can negatively impact survival. We conducted a systematic review to evaluate adherence to AHT in early BC and clinical factors associated with non-adherence. Methods: Using search terms such as 'adherence', ‘adjuvant treatment’ and ‘breast cancer’, we identified a total of 843 articles published between 1990 and 2012 of which 43 met our eligibility criteria (7 prospective and 36 retrospective). Pooled estimates of adherence and non-adherence were derived by random effects models. Heterogeneity was assessed by the Cochran's Q and I-squared statistics. To detect for potential publication bias, Egger’s test was conducted. Results: The median rates of adherence, compliance, continuation and persistence to AHT were 77%, 79%, 69% and 81%, respectively. Compared to individuals aged <60 years, patients aged >/=60 years had a significantly higher likelihood of non-adherence (RR 1.59, 95% CI 1.27-2.00, p<0.001). Likewise, non-white patients were more likely to poorly adhere to AHT when compared to their white counterparts (RR 1.39, 95% CI 1.00-1.94, p=0.05). Additionally, prior or concurrent receipt of chemotherapy (RR 1.41, 95% CI 1.06-1.88, p=0.018) and radiation (RR 1.79, 95% CI 1.14-2.81, p=0.011) were independently associated with worse adherence. Interestingly, those with more than a moderate income level defined as >$30,000 per annum had a greater likelihood of poor adherence to AHT when compared to low income earners (RR 1.12, 95% CI 1.01-1.24, p<0.024). There was moderate to high heterogeneity across studies, but there was no evidence of publication bias. Conclusions: In this systematic review, adherence rates to AHT for early BC were suboptimal, underscoring the importance of increased vigilance and need for strategies that promote compliance with proven therapies. Poor adherence patterns were observed in specific patient subsets, but these were not limited to only marginalized populations. Prospective trials of adherence interventions that stratify by these patient subgroups are warranted.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".