Neonatal phototherapy and future risk of childhood cancer
Bibliographic record
Abstract
We sought to determine if neonatal phototherapy is associated with a greater risk of childhood cancer. We conducted a retrospective cohort study of 786,998 infants born in hospitals of Quebec, Canada between 2006 and 2016, with 4,660,868 person-years of follow-up over an 11-year period. The exposures were neonatal phototherapy (32,314 or 4.1% of infants) and untreated jaundice (91,855 or 11.7% of infants). The outcome was hospitalization for solid or hematopoietic childhood tumours between 2 months and 11 years of age. We used Cox proportional hazards regression models to compute hazard ratios (HR) and 95% confidence intervals (CI) for the association of phototherapy with childhood cancer, adjusted for infant characteristics. The incidence of childhood cancer was higher for infants with phototherapy (25.1 per 100,000 person-years) and untreated jaundice (23.0 per 100,000) compared to unexposed infants (21.6 per 100,000). Phototherapy appeared to be associated with late onset solid tumours, including brain/central nervous system cancers. Between age 4 and 11 years, children who received neonatal phototherapy had more than 2 times the risk of any solid tumour compared to unexposed children (HR 2.26, 95% CI 1.34-3.81). Results were similar for phototherapy compared against untreated jaundice. A similar trend was however less apparent for hematopoietic cancer. We conclude that neonatal phototherapy may be associated with a slightly increased risk of solid tumours in childhood, but cannot rule out an effect of bilirubin. Minimizing unnecessary exposure to phototherapy through adherence to recommended thresholds for treatment is encouraged.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".