Access to Hematopoietic Stem Cell Transplantation among Pediatric Patients with Acute Lymphoblastic Leukemia: A Population-Based Analysis
Bibliographic record
Abstract
Access to hematopoietic stem cell transplantation (HSCT) in pediatric acute lymphoblastic leukemia (ALL) primarily depends on disease-related factors but may be influenced by social and economic determinants. We included all children aged < 15 years with newly diagnosed ALL in Canada between 2001 and 2018 using the Cancer in Young People in Canada national registry. We examined factors potentially associated with the likelihood of receiving HSCT using univariate and multivariable logistic regression models. A total of 3992 patients with newly diagnosed ALL were included. Three hundred twenty-five (8.1%) received an HSCT and formed the transplant cohort. In multivariable analysis factors independently associated with an increased odds of receiving HSCT were male sex (odds ratio [OR], 1.42; 95% confidence interval [CI], 1.05 to 1.93), initial WBC ≥ 50,000 × 10 9 /L (OR, 1.58; 95% CI, 1.09 to 2.28), mixed phenotype acute leukemia relative to B-precursor ALL (OR, 34.32; 95% CI, 16.64 to 70.79), T cell relative to B-precursor ALL (OR, 1.77; 95% CI, 1.07 to 2.91), unfavorable relative to standard cytogenetics (OR, 3.96; 95% CI, 2.56 to 6.12), and relapse before HSCT (OR, 32.77; 95%, 23.89 to 44.96). No association was found between race, neighborhood income quintile or region at diagnosis, and receipt of HSCT. Diagnosis at an HSCT treating center (OR, 1.51; 95% CI, 1.09 to 2.09) and residential distance from the ALL treating center (OR, 1.84 for ≥300 km compared with <100 km; 95% CI, 1.17 to 2.91) were associated with higher odds of receiving HSCT. In a publically funded healthcare system, children with ALL had equitable access to HSCT, which was largely governed by biologic disease-related factors. Patients diagnosed at an HSCT performing center and patients who live farthest away from their treatment center had higher odds of receiving HSCT, although the effect was small, possibly suggesting preferential referral to HSCT for some patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".