Assessing adherence to oral chemotherapy using different measurement methods: Lessons learned from capecitabine
Bibliographic record
Abstract
PURPOSE: Adherence to oral medication is important in oncology. Few studies have evaluated adherence with cancer agents such as capecitabine, which is given on a complicated schedule. Furthermore, little guidance exists regarding the best methods for monitoring adherence with oral cancer drugs. The purpose of our study was to evaluate adherence to capecitabine using several accepted measures. PATIENTS AND METHODS: Patients treated with capecitabine for gastrointestinal cancers were included in this prospective cohort study. Adherence was evaluated during two consecutive cycles of capecitabine using three assessment methods: self-report, pill count, and use of a microelectronic monitoring system. The primary endpoint was proportion of patients adherent to capecitabine (>80% of adherence according to the three methods of measurement); the secondary objective was to compare the three methods of measurement. RESULTS: Nineteen patients were accrued to this study. Further accrual was stopped after the first planned analysis, because 18 and 19 patients were adherent by self-report and pill count, respectively. The overall adherence rates were 99, 100, and 61% with self-report, pill count, and microelectronic monitoring system cap, respectively. Ten (53%) patients were classified as nonadherent (<80% of adherence according to at least one method of measurement), but four of them transferred their pills into another medication container suggesting that measurement of adherence using microelectronic monitoring system technology may not be useful. CONCLUSION: While we did not identify a major adherence issue with capecitabine in our study, it provides insight into problems associated with measurement of adherence in oncology and suggests that combining measures of adherence maximizes accuracy.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".