Characterizing the cutaneous late effects of allogeneic hematopoietic stem cell transplantation: A systematic review
Bibliographic record
Abstract
BACKGROUND: There is a well-documented risk of secondary cutaneous malignancies following allogeneic hematopoietic stem cell transplant (HSCT), but data on risk in pediatric populations are limited. The objective of this study is to perform a systematic review of reported features and outcomes of skin cancers in pediatric allogeneic HSCT recipients. METHODS: MEDLINE, EMBASE, CINAHL, Cochrane, and Web of Science were systematically searched (Prospero CRD42022342139). Studies reporting cutaneous cancer outcomes were included if the age at transplant was ≤19 years. Titles, abstracts, and full-text articles were screened in duplicate. RESULTS: Out of 824 citations that were screened, 12 articles were selected for analysis. The final sample included 67 pediatric HSCT recipients, comprising 65 allogeneic transplant recipients and 2 cases of HSCT with an unknown donor type. The median age at transplant and skin cancer diagnosis were 7.4 and 13 years, respectively. Out of the 67 pediatric HSCT recipients, some patients developed more than one lesion, resulting in 71 lesions. The most common skin cancer type was cutaneous squamous cell carcinoma (32 lesions), followed by basal cell carcinoma (25 lesions). The median latency period between HSCT and skin cancer diagnosis ranged from 0 to 29 years. Identified risk factors for skin cancers included younger age at the time of transplant, exposure to total body irradiation, prolonged post-transplant immunosuppression, graft versus host disease, and sunburn. CONCLUSION: Skin cancers are reported in pediatric allogeneic HSCT recipients, and the risk appears to be increased. More data are needed to better characterize this risk.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".