Matched cohort study of hospitalization in children who have siblings with cancer
Bibliographic record
Abstract
BACKGROUND: Health outcomes of children in families affected by cancer are poorly understood. The authors assessed the risk of hospitalization in children who have a sibling with cancer. METHODS: This was a longitudinal cohort study in which 1600 children who had a sibling with cancer were matched to 32,000 children who had unaffected siblings in Quebec, Canada, from 2006 to 2020. The exposure of interest was having a sibling with cancer. Outcomes included hospitalization for pneumonia, asthma, fracture, and other morbidities any time after the sibling was diagnosed with cancer. The children were followed over time, and Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the impact of having a sibling with cancer on the risk of hospitalization before age 14 years, adjusted for patient characteristics. RESULTS: Children who had a sibling with cancer had an increased risk of hospitalization compared with unaffected children (HR, 1.15; 95% CI, 1.02-1.29). Conditions associated with a greater risk of hospitalization included pneumonia, hemangioma, other skin conditions, sleep apnea, and inflammatory bowel disease. The risk of hospitalization was greatest for children whose older sibling had cancer (HR, 1.16; 95% CI, 1.01-1.32) and for children whose sibling had hematopoietic cancer (HR, 1.22; 95% CI, 1.01-1.48). CONCLUSIONS: Children who have a sibling with cancer are at risk of hospitalization for conditions such as pneumonia, inflammatory bowel disease, and other morbidities. Families affected by childhood cancer may benefit from additional support to facilitate care for all children in the family. LAY SUMMARY: Little is known about the health of children who have a brother or sister with cancer. The authors studied the types of hospitalization experienced by children who have siblings with cancer. The results indicated that having a sibling with cancer increased the chance of being hospitalized for pneumonia and other conditions that could have been preventable. The results also indicated that children who had an older sibling with cancer or a sibling with blood cancer had a greater chance of being hospitalized. The findings highlight the importance of providing timely care for children in families affected by childhood cancer.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".