Longitudinal adherence to surveillance for late effects of cancer treatment: a population-based study of adult survivors of childhood cancer
Bibliographic record
Abstract
BACKGROUND: Adult survivors of childhood cancer are at elevated risk of morbidity and mortality compared to the general population, but their adherence to lifelong periodic surveillance is suboptimal. We aimed to examine adherence to surveillance guidelines for high-yield tests and identify risk factors for nonadherence in adult survivors of childhood cancer. METHODS: In this retrospective, population-based cohort study, we used health care administrative data from Ontario, Canada, to identify adult survivors of childhood cancer diagnosed between 1986 and 2014 who were at elevated risk of therapy-related colorectal cancer, breast cancer, or cardiomyopathy. Using a Poisson regression framework, we assessed longitudinal adherence and predictors of adherence to the Children's Oncology Group surveillance guideline. RESULTS: Among 3241 survivors, 327 (10%), 234 (7%), and 3205 (99%) were at elevated risk for colorectal cancer, breast cancer, and cardiomyopathy, respectively. Within these cohorts, only 13%, 6%, and 53% were adherent to recommended surveillance as of February 2020. During a median follow-up of 7.8 years, the proportion of time spent adherent was 14% among survivors at elevated risk for colorectal cancer, 10% for breast cancer, and 43% for cardiomyopathy. Significant predictors of adherence varied across the risk groups, but higher comorbidity was associated with adherence to recommended surveillance. INTERPRETATION: Survivors of childhood cancer in Ontario are rarely up to date for recommended surveillance tests. Tailored interventions beyond specialized clinics are needed to improve surveillance 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".