Dermatological manifestations of inherited cancer syndromes in children
Bibliographic record
Abstract
Various cutaneous signs presenting in childhood, for example café-au-lait macules, may have systemic cancer associations. Indeed, this may be the first manifestation of the underlying cancer predisposition. The syndromes covered in this review fall into four main categories: (i) DNA damage processing defects including Fanconi anaemia, ataxia telangiectasia, Bloom syndrome, Rothmund-Thomson syndrome, constitutional mismatch repair defects and xeroderma pigmentosum; (ii) signalling pathway defects, including naevoid basal cell carcinoma and Costello syndromes; (iii) primary immunodeficiency syndromes; and (iv) syndromes that do not fit this molecular classification, such as X-linked dyskeratosis congenita. This review focuses on the dermatological findings of these conditions. Some of these conditions exhibit a milder heterozygous phenotype and this should be elicited in the family history. Where the dermatological findings are subtle, a targeted family history can provide clues towards making a diagnosis. Nondermatological features of each condition are summarized too, together with molecular testing strategies, which will direct genetic counselling and screening. This review will enable the dermatologist and other clinicians in the early recognition and molecular confirmation of underlying cancer-predisposing syndromes. This allows the possibility of surveillance and prevention strategies to be initiated in a timely manner, in affected children and other at-risk family members.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".