The prevalence, complications, and risk factors for infantile hemangioma: a systematic review and meta‐analysis
Bibliographic record
Abstract
The epidemiological landscape of infantile hemangioma (IH) has been extensively explored through diverse data sources; however, a scarcity of systematically pooled and quantified evidence from comprehensive global studies persists. In this meta-analysis, we systematically review available literature to elucidate the prevalence, distribution of lesions, complications, and risk factors associated with IH. A meticulous search encompassing the Cochrane Library, PubMed, Embase, and Web of Science identified 3206 records, of which 55 studies met the inclusion criteria. We found that the overall prevalence of IH is 2.8% [95% confidence interval (CI): 1.5-4.4%] (31,274,396 infants), and IH was located more frequently in the head and neck with a prevalence of 47.4% (95% CI: 39.5-55.4%). The overall prevalence of complications of IH is 24.3% (95% CI: 18.6-30.5%), ulceration is 16.0% (95% CI: 10.4-21.2%), bleeding is 5.6% (95% CI: 3.3-8.5%), visual impairment is 5.6% (95% CI: 3.0-8.9%), infection is 2.8% (95% CI: 1.5-4.8%), subglottic obstruction is 1.5% (95% CI: 0.5-3.0%), respectively. Through 27 studies, we have evaluated 35 factors encompassing perinatal factors, socioeconomic factors, maternal complications, drug factors, and antepartum procedures, and identified 18 risk factors that increase the prevalence of IH. These findings can greatly assist clinicians and family members in effectively evaluating the risk of IH, and determining whether pregnant women should undergo intensified monitoring or preventive measures.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".