Morbidity and healthcare use among mothers of children with cancer: A population‐based study
Bibliographic record
Abstract
BACKGROUND: The impact of a child's cancer diagnosis on subsequent maternal physical health is unclear. METHODS: We identified all Ontario children diagnosed less than 18 years with cancer between 1992 and 2017. Linkage to administrative databases identified mothers who were matched to population controls. We identified physical health conditions, acute healthcare use, and preventive healthcare use through validated algorithms using healthcare data, and compared them between exposed (child with cancer) and unexposed mothers. Predictors of health outcomes were assessed among exposed mothers. RESULTS: We identified 5311 exposed mothers and 19,516 matched unexposed mothers. For exposed mothers, median age at last follow-up was 48 years, (interquartile range: 42-53). Exposed mothers had an increased risk of cancer (hazard ratio [HR] 1.2, 95% confidence interval [95% CI]: 1.0-1.5, p = .03), but not of any other adverse physical outcomes or of increased acute healthcare use. Exposed mothers were more likely to receive influenza vaccinations (odds ratio 1.4, 95% CI: 1.3-1.5, p < .0001), and underwent cancer screening at the same rate as unexposed mothers. Among exposed mothers, bereavement was associated with a subsequent increased risk of cancer (HR 1.7, 95% CI: 1.2-2.5, p = .004) and death (HR 2.2, 95% CI: 1.2-4.1, p = .01). CONCLUSION: Mothers of children with cancer are at increased risk of developing cancer, but not of other adverse physical health outcomes, and were equally or more likely to be adherent to preventive healthcare practices. Bereaved mothers were at increased risk of subsequent cancer and death. Interventions targeting specific subpopulations of mothers of children with cancer or focused on screening for specific cancers may be 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".