Neonatal Cancer Epidemiology and Outcome: A Retrospective Study
Bibliographic record
Abstract
OBJECTIVE: Our study aimed at describing neonatal cancer incidence, distribution by type, location, outcome, and long-term toxicity, by comparison with tumors occurring later in infancy. METHODS: The authors led a single-center retrospective analysis of 118 cases of tumors diagnosed in the first year of life and compared tumors' types incidence, presentation, location, and outcome according to age group at diagnosis (below or over 28 d of life). RESULTS: The most frequent neonatal tumor types in our series were germ cell tumors, mainly teratoma, followed by neuroblastoma and renal tumors, whereas in children below 1 year of age, brain tumors, neuroblastoma, and leukemia were the most common types. Genetic predisposition syndromes were present in 14% of these infants and antenatal sonography enabled 68% of diagnosis for tumors presenting at birth. Other patients presented with mass syndrome, hydrops, or skin lesions. Six percent of neonates with cancer died from their malignancies, and up to 18% experienced a chronic health condition as a consequence of therapy. CONCLUSIONS: Tumor pattern differs in neonates and infants, with a higher percentage of benign tumors in children below 28 days of life. Yet, long-term therapy-related toxicity is significant in younger patients. Enhancing knowledge of neonatal tumors, their epidemiology, clinical presentation, genetic background, and prognosis should help promote better management and introduce follow-up programs to improve surviving rates and the quality of life of survivors.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".