Hospital‐related morbidity among childhood cancer survivors in British Columbia, Canada: Report of the childhood, adolescent, young adult cancer survivors (CAYACS) program
Bibliographic record
Abstract
Our study examines inpatient, hospital-related morbidity in a geographically-defined cohort of long-term cancer survivors diagnosed before age 20 years in the province of British Columbia (BC), Canada. A total of 1374 survivors diagnosed from 1981 to 1995 surviving at least 5-years postdiagnosis, and a matched sample of 13,740 BC residents, were identified from population registers, and linked to provincial hospitalization records from 1986 to 2000. Logistic regression was used to assess relative risk and effect of sociodemographic, clinical, and temporal factors on risk. Approximately 41% of survivors vs. 17% of the population sample had at least one type of hospitalization-related late morbidity in the observation period (adjusted RR 4.1, 95% CI 3.7-4.5). Those at highest risk were survivors of leukemia (RR 4.8, 95% CI 4.0-5.8), central nervous system tumors (RR 4.8, 95% CI 4.0-5.8), bone and soft tissue sarcomas (RR 4.9, 95% CI 3.8-6.2), and kidney cancer (RR 4.9, 95% CI 3.4-7.0). Adjusted relative risk was elevated for all types of morbidity except pregnancy and birth complications, and highest for neoplasms (including second primary cancers) (RR 21.7, 95% CI 16.3-28.7). Morbidity was elevated for all combinations of primary treatment and highest for those with previous radiation, chemotherapy, and surgery (RR 7.1, 95% CI 5.5-9.0). Over time, morbidity for late effects other than neoplasms became more prevalent. These results suggest that survivors are at increased ongoing risk of many types of hospital-related late morbidity, implying that long-term monitoring for multiple health problems is warranted.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".