Differences in Prescription Drug use Among 5-year Survivors of Childhood, Adolescent, and Young Adult Cancer and the General Population in British Columbia, Canada
Bibliographic record
Abstract
In this project, we analyze the prescription drug use of childhood, adolescent, and young adult cancer survivors identified by the CAYACS program in BC.Understanding the patterns of prescription use and factors associated with the tendency to be on prescriptions is important to policy and health care planners.Since data on actual prescription usage are not available, we use prescription dispensing data as a proxy.We examine the differences in prescription use between survivors and matched controls selected from the general population, and assess the impact of age and other clinical and sociodemographic factors on prescription use.Specifically, we model subjects' on-/off-prescription status by a first-order Markov transition model, and handle the between-subject heterogeneity using a random effect.Our method captures the differences in prescription drug use between survivors and the general population, as well as differences within the survivor population.Our results show that survivors tend to exhibit a higher probability of going on prescriptions compared to the general population over the course of their lifetime.Further, females appear to have a higher probability of going on prescriptions than males over the course of their lifetime.A simulation study is conducted to assess the performance of the estimators of the model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".