Subsequent neoplasms and late mortality in children undergoing allogeneic transplantation for nonmalignant diseases
Bibliographic record
Abstract
We examined the risk of subsequent neoplasms (SNs) and late mortality in children and adolescents undergoing allogeneic hematopoietic cell transplantation (HCT) for nonmalignant diseases (NMDs). We included 6028 patients (median age, 6 years; interquartile range, 1-11; range, <1 to 20) from the Center for International Blood and Marrow Transplant Research (1995-2012) registry. Standardized mortality ratios (SMRs) in 2-year survivors and standardized incidence ratios (SIRs) were calculated to compare mortality and SN rates with expected rates in the general population. Median follow-up of survivors was 7.8 years. Diagnoses included severe aplastic anemia (SAA; 24%), Fanconi anemia (FA; 10%), other marrow failure (6%), hemoglobinopathy (15%), immunodeficiency (23%), and metabolic/leukodystrophy syndrome (22%). Ten-year survival was 93% (95% confidence interval [95% CI], 92% to 94%; SMR, 4.2; 95% CI, 3.7-4.8). Seventy-one patients developed SNs (1.2%). Incidence was highest in FA (5.5%), SAA (1.1%), and other marrow failure syndromes (1.7%); for other NMDs, incidence was <1%. Hematologic (27%), oropharyngeal (25%), and skin cancers (13%) were most common. Leukemia risk was highest in the first 5 years posttransplantation; oropharyngeal, skin, liver, and thyroid tumors primarily occurred after 5 years. Despite a low number of SNs, patients had an 11-fold increased SN risk (SIR, 11; 95% CI, 8.9-13.9) compared with the general population. We report excellent long-term survival and low SN incidence in an international cohort of children undergoing HCT for NMDs. The risk of SN development was highest in patients with FA and marrow failure syndromes, highlighting the need for long-term posttransplantation surveillance in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".