Childhood, adolescent, and young adult cancer survivors (CAYACS) research program of British Columbia: Data linkage: Results to date
Bibliographic record
Abstract
9555 Background: Long-term survivors of childhood and adolescent cancers are at risk for late mortality and morbidity. Using database linkages we assessed the extent of these issues and health care utilization in a population based cohort in British Columbia. Methods: Retrospective cohorts of 3,483 survivors (>5 years from diagnosis), and representative comparison groups, have been identified from population-based registries. Linkages were made with administrative databases of risk factors and outcomes. Late mortality, second cancers, late morbidity, health services utilization, continuity of care, and educational outcomes, among those diagnosed before age 20 between 1970 to 1995, and followed to 2000, have been examined. Results: Survivors experienced a 9-fold increase in mortality (SMR 9.1, 95% CI 7.8–10.5). Risk of developing a second cancer was 5 times higher than in the general population (SIR 5.0, 95% CI 3.8–6.5). Survivors had three times the odds of being hospitalized (OR 2.97, 95% CI 2.56–3.45) in a three-year period (1998–2000). Survivors were significantly more likely than the population group to consult any physician (excluding oncologists) (adj. RR 1.61, 95% CI 1.51–1.70). Survivors were found to experience a drop in continuity of primary health care as they aged and transitioned into adult care. Survivors were significantly more likely than their peers to receive special education (32.5% vs. 14.1%), most significantly among CNS survivors who received cranial irradiation. Conclusions: Survivors of childhood and adolescent cancers have severe long term health issues and increased health care utilization. Survivors of CNS tumors were at highest risk of poor health and educational outcomes measured. Data linkage provides useful insights for survivorship research. No significant financial relationships to disclose.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".