Cumulative incidence estimates for solid tumors after HCT in the CIBMTR and California Cancer Registry
Bibliographic record
Abstract
ABSTRACT: Compared with the general population, hematopoietic cell transplantation (HCT) survivors are at elevated risk for developing solid subsequent neoplasms (SNs). The Center for International Blood and Marrow Transplant Research (CIBMTR) is a key resource for quantifying solid SN incidence following HCT, but the completeness of SN ascertainment is uncertain. Within a cohort of 18 450 CIBMTR patients linked to the California Cancer Registry (CCR), we evaluated the completeness of solid SN data reported to the CIBMTR from 1991 to 2018 to understand the implications of using CIBMTR data alone or combined with CCR data to quantify the burden of solid SNs after HCT. We estimated the cumulative incidence of developing a solid SN, accounting for the competing risk of death. Within the cohort, solid SNs were reported among 724 patients; 15.6% of these patients had an SN reported by CIBMTR only, 36.9% by CCR only, and 47.5% by both. The corresponding cumulative incidence of developing a solid SN at 10 years following a first HCT was 4.0% (95% confidence interval [CI], 3.5-4.4) according to CIBMTR data only, 5.3% (95% CI, 4.9-5.9) according to CCR data only, and 6.3% (95% CI, 5.7-6.8) according to both sources combined. The patterns were similar for allogeneic and autologous HCT recipients. Linking detailed HCT information from CIBMTR with comprehensive SN data from cancer registries provides an opportunity to optimize SN ascertainment for informing follow-up care practices and evaluating risk factors in the growing population of HCT 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".