Risk of tumorigenesis in overgrowth syndromes: A comprehensive review
Bibliographic record
Abstract
Overgrowth syndromes (OGS) comprise a heterogeneous group of disorders in which the main characteristic is that either weight, height, or head circumference is 2-3 standard deviations (SD) above the mean for sex and age. A striking feature of OGS is the risk of neoplasms. Here, the relative frequency of specific tumors in each OGS, topographic location, and age of appearance is determined by reviewing published cases. In some OGS (Perlman, Beckwith-Wiedemann, and Simpson-Golabi-Behmel syndromes and hemihyperplasia) more than 94% of tumors appeared in the abdomen usually before 10 years of age, mainly embryonal in type. In Perlman syndrome, only Wilms tumor has been recorded, whereas in Sotos syndrome, lympho-hematologic tumors are most frequent. Based on literature review, a specific schedule protocol for tumor screening is suggested for each OGS. A schedule with different intervals and specific tests is proposed for a more rational cost/benefit program for these disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".