Measuring compliance to capecitabine in colon cancer: A comparison of administrative data and medical records.
Bibliographic record
Abstract
531 Background: Studies of administrative data indicate that compliance with oral anti-neoplastic agents, including capecitabine, is suboptimal. Using a population-based cohort of colon cancer, our aim was to evaluate the proportion of patients deemed non-compliant that actually had a reasonable indication for not adhering to their prescribed oral systemic therapy. Methods: Consecutive patients diagnosed with stage III colon cancer from 2008 to 2010, referred to any 1 of 5 regional cancer centers in British Columbia, Canada, and who initiated at least 1 cycle of oral adjuvant capecitabine within 12 weeks of curative resection were reviewed. Administrative data from the provincial oncology pharmacy database were analyzed to assess for non-compliance, which was defined as any prescription refill delays of >/=1 week from the end date of the preceding cycle. Electronic medical records were abstracted to examine the factors, if any, which contributed to non-compliance. We compared administrative vs. medical record data to determine the level of concordance and the appropriateness of non-compliance. Results: We included 752 patients: median age was 70 years (IQR 35-87), 56% were men, and 86% had ECOG 0/1. Administrative data showed that 413 patients were non-compliant: 230 (56%) and 183 (44%) had 1 and >/=2 late refills, respectively. In this group, a total of 2095 cycles of capecitabine were delivered among which 688 (33%) treatment delays occurred. No differences in baseline characteristics were observed between individuals who were compliant and those who were not (all p>0.05). Of the 688 prescription delays that were ascertained from administrative data, medical records demonstrated that 30 (4%) were misclassified. From the remainder, the majority were attributable to valid reasons, including: 339 (49%) toxicities necessitating time off therapy; 170 (25%) physician discretion; 40 (6%) travel requiring adjustment to treatment schedule; and 6 (1%) patient refusal. Only 103 (15%) cases were considered as true non-compliance. Conclusions: Using administrative data alone to measure oral drug compliance without corroborating with medical records may overestimate the degree of non-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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".