Country-Level Macroeconomic Indicators Predict Early Post-Allogeneic Hematopoietic Cell Transplantation Survival in Acute Lymphoblastic Leukemia: A CIBMTR Analysis
Bibliographic record
Abstract
For patients with acute lymphoblastic leukemia (ALL), allogeneic hematopoietic cell transplantation (alloHCT) offers a potential cure. Life-threatening complications can arise from alloHCT that require the application of sophisticated health care delivery. The impact of country-level economic conditions on post-transplantation outcomes is not known. Our objective was to assess whether these variables were associated with outcomes for patients transplanted for ALL. Using data from the Center for Blood and Marrow Transplant Research, we included 11,261 patients who received a first alloHCT for ALL from 303 centers across 38 countries between the years of 2005 and 2013. Cox regression models were constructed using the following macroeconomic indicators as main effects: Gross national income per capita, health expenditure per capita, and Human Development Index (HDI). The outcome was overall survival at 100 days following transplantation. In each model, transplants performed within lower resourced environments were associated with inferior overall survival. In the model with the HDI as the main effect, transplants performed in the lowest HDI quartile (n = 697) were associated with increased hazard for mortality (hazard ratio, 2.42; 95% confidence interval, 1.64 to 3.57; P < .001) in comparison with transplants performed in the countries with the highest HDI quartile. This translated into an 11% survival difference at 100 days (77% for lowest HDI quartile versus 88% for all other quartiles). Country-level macroeconomic indices were associated with lower survival at 100 days after alloHCT for ALL. The reasons for this disparity require further investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".