Does drug cost drive drug adherence? The designer drug phenomenon.
Bibliographic record
Abstract
6084 Background: Cancer patients (pts) are on many medications, both for malignancy and supportive therapies. The cost of oral medications are funded by either the pt, public, or private mechanisms. Low adherence rates are observed in oral treatments, and non-adherence is the primary cause of treatment failures. To our knowledge, cost-related adherence to oral therapy in the context of malignancy has not been studied extensively in the existing literature. We assessed the relationships between oral medication costs and adherence rates. Methods: This cohort study enrolled 453 pts at 3 outpatient heme/onc clinics in the Greater Toronto Area. A 7-item survey was designed to assess pt demographics, self-reported adherence to oral medication, type of drug coverage (private payer, public payer, self payer) and patients' perceived cost of oral drugs. Oral medications were recorded and actual monthly costs were calculated. Descriptive statistics were used to describe frequencies. Spearman Rank Order Correlations and Chi-Square Analyses were used to examine relationships between variables. Results: Of 453 pts, 50% had a private drug plan, 24% paid out of pocket, 44% had government funding and 4% reported their physician had arranged funding. 51% of pts had oral drug costs of ≥ $100/month. Self reported adherence to prescribed oral medications was 80%. As the cost of prescribed medications increased, so did self reported adherence (r=0.144, p=0.002). There was also a significant relationship between drug coverage and oral drug costs (c²=23.78, df=12, p=0.02). Pts paying out of pocket were significantly less likely than all other pts to have oral drug costs of ≥$500/month (11% vs 19%) Conclusions: As oral drug costs increase, so does likelihood of adhering to prescribed regimen. This implies that further pt education about the efficacy and importance of medications may help with adherence, regardless of cost. It is possible that pts are provided with more education regarding newer and more expensive agents than they are regarding older and cheaper agents, regardless of efficacy. Disparities observed in medication costs between those with private drug plans vs. those without suggests financial restrictions may affect prescription patterns in this group.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".