Comparison of Vital Status, Cause of Death, and Follow-Up after Hematopoietic Cell Transplantation in Linked Center for International Blood and Marrow Transplant Research and California Cancer Registry Data, 1991 to 2018
Bibliographic record
Abstract
Assessing outcomes following hematopoietic cell transplantation (HCT) poses challenges due to the necessity for systematic and often prolonged patient follow-up. Linking the HCT database of the Center for International Blood and Marrow Transplant Research (CIBMTR) with cancer registry data may improve long-term outcome ascertainment, but the reliability of mortality data in death certificates from cancer registries among HCT recipients remains unknown. We compared the classification of vital status and primary cause of death (COD), as well as the length of follow-up between the CIBMTR and California Cancer Registry (CCR) to assess the possibility of supplementing the CIBMTR with cancer registry data. This retrospective study leveraged a linked CIBMTR-CCR dataset. We included patients who were California residents at the time of HCT and received a first allogeneic (allo) or autologous (auto) HCT for a hematologic malignancy diagnosed during 1991-2016. Follow-up was through 2018. We analyzed 18,450 patients (alloHCT, n = 8232; autoHCT, n = 10,218). The Vital status agreement was 97.7% for alloHCT and 97.2% for autoHCT. Unknown COD was higher in CIBMTR (12.9%) than in CCR (1.6%). After excluding patients with unknown COD information, the overall agreement of primary COD (cancer versus noncancer) was 53.7% for alloHCT and 83.2% for autoHCT. This agreement was lower within the first 100 days post-HCT (alloHCT, 31.0%; autoHCT, 54.6%). Compared with CIBMTR, deaths due to cancer were higher in CCR (alloHCT, 90.0%; autoHCT, 90.1% versus alloHCT, 47.3%; autoHCT, 82.5% in CIBMTR). CIBMTR reports more frequently noncancer-related deaths, including graft-versus-host disease and infections. The cumulative incidence of cancer-specific mortality at 20 years differed, particularly for alloHCT (CCR, 53.7%; CIBMTR, 27.6%). The median follow-up among alive patients was longer in CCR (alloHCT, 6.0 years; autoHCT, 4.7 years) than in CIBMTR (alloHCT, 5.0 years; autoHCT, 3.8 years). Our findings highlight the completeness of vital status data in CIBMTR but reveal substantial disagreement in primary COD. Consequently, caution is required when interpreting HCT studies that use only death certificates to estimate cause-specific mortality outcomes. Improving the accuracy of COD registration and follow-up completeness by developing communication pathways between cancer registries and hospital-based cohorts may enhance our understanding of late effects and long-term outcomes among 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".