Late morbidity leading to hospitalization among 5‐year survivors of young adult cancer: A report of the childhood, adolescent and young adult cancer survivors research program
Bibliographic record
Abstract
To estimate the risk of late morbidity leading to hospitalization among young adult cancer 5-year survivors compared to the general population and to examine the long-term effects of demographic and disease-related factors on late morbidity, a retrospective cohort of 902 five-year survivors of young adult cancer diagnosed between 1981 and 1999 was identified from British Columbia (BC) Cancer Registry. A matched comparison group (N = 9020) was randomly selected from the provincial health insurance plan. All hospitalizations until the end of 2006 were determined from the BC health insurance plan hospitalization records. The Poisson regression model was used to estimate the rate ratios for late morbidity leading to hospitalization except pregnancy after adjusting for sociodemographic and clinical risk factors. Overall, 455 (50.4%) survivors and 3,419 (37.9%) individuals in the comparison group had at least one type of late morbidity leading to hospitalization. The adjusted risk of this morbidity for survivors was 1.4 times higher than for the comparison group (95% CI = 1.22-1.54). The highest risks were found for hospitalization due to blood disease (RR = 4.2; 95% CI = 1.98-8.78) and neoplasm (RR = 4.3; 95% CI = 3.41-5.33). Survivors with three treatment modalities had three-fold higher risk of having any type of late morbidity (RR = 3.22; 95% CI = 2.09-4.94) than the comparators. These findings emphasize that young adult cancer survivors still have high risks of a wide range of late morbidities.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".