Assessing the Influence of Different Comorbidities Indexes on the Outcomes of Allogeneic Hematopoietic Stem Cell Transplantation in a Developing Country
Bibliographic record
Abstract
Although the application of Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) has enabled better prediction of transplant-related mortality (TRM) in allogeneic hematopoietic stem cell transplants (AHSCT), data from developing countries are scarce. This study prospectively evaluated the HCT-CI and the Adult Comorbidity Evaluation (ACE-27), in its original and in a modified version, as predictors of post-transplant complications in adults undergoing a first related or unrelated AHSCT in Brazil. Both bone marrow (BM) and peripheral blood stem cells (PBSC) as graft sources were included. We analyzed the cumulative incidence of granulocyte and platelet recovery, sinusoidal obstructive syndrome, acute and chronic graft-versus-host disease, relapse and transplant-related mortality, and rates of event-free survival and overall survival. Ninety-nine patients were assessed. Median age was 38 years (18-65 years); HCT-CI ≥ 3 accounted for only 8% of cases; hematologic malignancies comprised 75.8% of the indications for AHSCT. There was no association between the HCT-CI or the original or modified ACE-27 with TRM or any other studied outcomes after AHSCT. These results show that, in the population studied, none of the comorbidity indexes seem to be associated with AHSCT outcomes. A significantly low frequency of high-risk (HCT-CI ≥ 3) in this Brazilian population might justify these results.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".