Hematopoietic stem cell transplant referral patterns for children with sickle cell disease vary among pediatric hematologist/oncologists’ practice focus: A Sickle Cell Transplant Advocacy and Research Alliance (STAR) study
Bibliographic record
Abstract
BACKGROUND: Hematopoietic stem cell transplantation (HSCT) provides a curative therapy for children severely affected by sickle cell disease (SCD). Rejection-free survival after matched sibling donor (MSD) HSCT is very high, but adoption of HSCT as a curative SCD therapy has been slow. In this study, we assess providers' perceptions about MSD HSCT for children with variable SCD severity, and determine the influence of provider characteristics on HSCT referrals. PROCEDURE: After our Institutional Review Board deemed the study exempt, American Society of Pediatric Hematology/Oncology Clinical Forum listserv subscribers and American Society of Hematology members who self-identified as pediatric hematologists/oncologists (PHO) were emailed a survey. Analysis was performed to describe and evaluate correlations between participant demographics (including practice focus within PHO) and likelihood of HSCT referral for each scenario. RESULTS: Spearman's rank correlation analysis did not reveal any significant relationship between demographic characteristics except practice focus and likelihood to refer to HSCT for any scenarios. Providers focused on SCD and HSCT were more likely to refer a child who had never been admitted to the hospital or had suboptimal adherence to hydroxyurea than general PHOs. A significantly higher proportion of all respondents would refer a child with β-thalassemia major (87%) than an asymptomatic child with HbSS (47%, P < .00001) or non-HbSS variant (23%, P < .00001). CONCLUSION: PSCD and HSCT physicians are more likely to refer for MSD HSCT in almost every condition than general PHO practitioners, likely because of increased awareness of long-term effects of SCD and safety of MSD HSCT for children with SCD.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".