Adherence With Oral Anticancer Therapies: Clinical Trial vs Real-world Experiences With a Focus on Prostate Cancer
Bibliographic record
Abstract
PURPOSE: In this review, we address adherence rates in clinical settings, barriers to compliance with dosing schedules, and potential strategies to overcome challenges in maintaining high levels of adherence. MATERIALS AND METHODS: Four studies reporting real-world adherence to prostate cancer medications, 52 studies describing barriers to adherence, and 16 studies on methods to minimize poor adherence were reviewed. RESULTS: Mean nonadherence rates of 25% to 51% have been identified in prostate cancer patients prescribed oral therapies, with higher rates in older patients. An extensive review of prostate cancer patients receiving gonadotropin hormone-releasing hormone agonist injections found an overall nonadherence rate of over 27%. Patients may encounter barriers to complying with dosing instructions related to the medication (eg, complex dosing schedules, the total burden of medication management, fasting or dietary requirements, high medication costs, adverse effects, and drug-drug interactions). Barriers may also be related to patient-specific factors (eg, suboptimal education regarding the importance of adherence, physical limitations and cognitive decline associated with advancing age, living alone without a care partner, high symptom burden, needle phobia, and comorbid mental disorders). Interventions to improve dosing adherence may include automated reminders, treatment diaries, educational materials, and the involvement of patients, family members, care partners, and health care teams. CONCLUSIONS: Many oral anticancer medications improve survival in men with prostate cancer, and therefore it is vital to establish good adherence by understanding the pitfalls that patients may encounter. In situations where both oral and injectable drugs are interchangeable, injections of long-acting drugs lead to fewer opportunities for dosing nonadherence than oral therapies. In contrast, oral medicines do not require scheduling for injections and travel for injection appointments. Therefore, maximizing adherence to all treatment regimens will reduce the chance of efficacy failures and likely lead to improved clinical outcomes.
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 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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".