Adherence to oral hormonal therapy in advanced prostate cancer: a scoping review
Bibliographic record
Abstract
Background: Orally administrated agents play a key role in the management of prostate cancer, providing a convenient and cost-effective treatment option for patients. However, they are also associated with adherence issues which can compromise therapeutic outcomes. This scoping review identifies and summarizes data on adherence to oral hormonal therapy in advanced prostate cancer and discusses associated factors and strategies for improving adherence. Methods: PubMed (inception to 27 January 2022) and conference databases (2020-2021) were searched to identify English language reports of real-world and clinical trial data on adherence to oral hormonal therapy in prostate cancer using the key search terms 'prostate cancer' AND 'adherence' AND 'oral therapy' OR respective aliases. Results: Most adherence outcome data were based on the use of androgen receptor pathway inhibitors in metastatic castration-resistant prostate cancer (mCRPC). Self-reported and observer-reported adherence data were used. The most common observer-reported measure, medication possession ratio, showed that the vast majority of patients were in possession of their medication, although proportion of days covered and persistence rates were considerably lower, raising the question whether patients were consistently receiving their treatment. Study follow-up for adherence was generally around 6 months up to 1 year. Studies also indicate that persistence may drop further with longer follow-up, especially in the non-mCRPC setting, which may be a concern when years of therapy are required. Conclusions: Oral hormonal therapy plays an important role in the treatment of advanced prostate cancer. Data on adherence to oral hormonal therapies in prostate cancer were generally of low quality, with high heterogeneity and inconsistent reporting across studies. Short study follow-up for adherence and focus on medication possession rates may further limit relevance of available data, especially in settings that require long-term treatment. Additional research is required to comprehensively assess adherence.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".