Pre‐eclampsia and risk of infantile haemangioma
Bibliographic record
Abstract
BACKGROUND: Infantile haemangioma is the most common tumour of infancy, but the association with pre-eclampsia is poorly understood. OBJECTIVES: We determined the relationship between variants of pre-eclampsia and risk of infantile haemangioma. METHODS: We carried out a retrospective cohort study of hospital data for all live births between 1989 and 2013 in Quebec, Canada. We identified 14 240 neonates with, and 1 930 564 without haemangioma before discharge, and determined whether early- or late-onset pre-eclampsia was documented on the maternal chart. We used log-binomial regression to compute prevalence ratios (PRs) and 95% confidence intervals (CIs) for the association between pre-eclampsia and infantile haemangioma, adjusted for maternal characteristics. RESULTS: The prevalence of any haemangioma was higher for pre-eclampsia than for no pre-eclampsia (81·3 vs. 72·9 per 10 000), with a PR of 1·15 (95% CI 1·06-1·25) after adjustment for maternal characteristics. Pre-eclampsia with onset before 34 weeks' gestation was associated with cutaneous (PR 2·32, 95% CI 1·68-3·21), noncutaneous (PR 3·66, 95% CI 2·49-5·37) and unspecified haemangioma (PR 2·49, 95% CI 1·77-3·49). However, the association between early-onset pre-eclampsia and haemangioma was attenuated once long neonatal length of hospital stays was accounted for. There was no association with late-onset pre-eclampsia after 34 weeks, and associations were weaker for other variants including severe pre-eclampsia and pre-eclampsia with low birthweight. CONCLUSIONS: Early-onset pre-eclampsia is associated with increased risk of haemangioma at birth, but detection bias due to longer hospital stays and closer follow-up may be part of the reason.
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 | 0.000 | 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".