Predictors of Loss to Follow-Up Among Pediatric and Adult Hematopoietic Cell Transplantation Survivors: A Report from the Center for International Blood and Marrow Transplant Research
Bibliographic record
Abstract
Follow-up is integral for hematopoietic cell transplantation (HCT) care to ensure surveillance and intervention for complications. We characterized the incidence of and predictors for being lost to follow-up. Two-year survivors of first allogeneic HCT (10,367 adults and 3865 children) or autologous HCT (7291 adults and 467 children) for malignant/nonmalignant disorders between 2002 and 2013 reported to the Center for International Blood and Marrow Transplant Research were selected. The cumulative incidence of being lost to follow-up (defined as having missed 2 consecutive follow-up reporting periods) was calculated. Marginal Cox models (adjusted for center effect) were fit to evaluate predictors. The 10-year cumulative incidence of being lost to follow-up was 13% (95% confidence interval [CI], 12% to 14%) in adult allogeneic HCT survivors, 15% (95% CI, 14% to 16%) in adult autologous HCT survivors, 25% (95% CI, 24% to 27%) in pediatric allogeneic HCT survivors, and 24% (95% CI, 20% to 29%) in pediatric autologous HCT survivors. Factors associated with being lost to follow-up include younger age, nonmalignant disease, public/no insurance (reference: private), residence farther from the tranplantation center, and being unmarried in adult allogeneic HCT survivors; older age and testicular/germ cell tumor (reference: non-Hodgkin lymphoma) in adult autologous HCT survivors; older age, public/no insurance (reference: private), and nonmalignant disease in pediatric allogeneic HCT survivors; and older age in pediatric autologous HCT survivors. Follow-up focusing on minimizing attrition in high-risk groups is needed to ensure surveillance for late effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".