Childhood, adolescent, and young adult cancer survivors research program of British Columbia: Objectives, study design, and cohort characteristics
Bibliographic record
Abstract
BACKGROUND: The Childhood, Adolescent, and Young Adult Cancer Survivors Research Program (CAYACS) has been established in the province of British Columbia (BC), Canada, to carry out research into late effects and survivor care in multiple domains, and to inform policy and practice. PROCEDURE: This program identifies a survivor cohort and comparison groups from population-based registries and links their records to population-based files of outcomes and outcome determinants, to create a research database and conduct studies of long-term outcomes and care. RESULTS: The initial cohort consisted of all 5-year survivors of cancer or a tumor diagnosed under age 25 years from 1970 to 1995, who were residents in BC at the time of diagnosis, and followed till 2000 (3,841 subjects). Seven percent have died, and 77% have treatment information available. Data on death and second cancer occurring in BC are available. Late morbidity and healthcare utilization information is available for 68% of survivors (79% of those diagnosed from 1981). Education outcomes are available for 71% of those born during 1978-1995 and diagnosed under age 15 years. CONCLUSIONS: Use of registries, administrative databases, and record linkage methodologies is a cost-effective and comprehensive means to conduct survivorship research. This program should add to knowledge of risks of late effects and impacts on care, inform development of strategies to manage risks, evaluate the effects of surveillance and interventions, and assess new risks as the cohort ages, more recent survivors enter the cohort, and treatments change.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".