Prognostic Performance of the Augmented Hematopoietic Cell Transplantation-Specific Comorbidity/Age Index in Recipients of Allogeneic Hematopoietic Stem Cell Transplantation from Alternative Graft Sources
Bibliographic record
Abstract
The Hematopoietic Cell Transplantation-Specific Comorbidity Index (HCT-CI) was developed and validated to weigh the burden of pretransplantation comorbidities and estimate their impact on post-transplantation risks of nonrelapse mortality (NRM). Recently, the HCT-CI was augmented by the addition of both age and the values of 3 markers: ferritin, albumin, and platelet count. So far, research involving The HCT-CI has been limited almost exclusively to recipients of allogeneic hematopoietic cell transplantation (HCT) from HLA-matched grafts. To this end, we sought to investigate the discriminative capacity of an augmented comorbidity/age index among 724 recipients of allogeneic HCT from HLA-mismatched (n = 345), haploidentical (n = 117), and umbilical cord blood (UCB; n = 262) grafts between 2000 and 2013. In the overall cohort, the augmented comorbidity/age index had a higher c-statistic estimate for prediction of NRM compared with the original HCT-CI (.63 versus .59). Findings were similar for recipients of HLA-mismatched (.62 versus .59), haploidentical (.60 versus .54), or UCB grafts (.65 versus .61). Compared with patients with an HCT-CI score ≥4, those with a score <4 had a higher survival rate among recipients of HLA-mismatched (55% versus 39%; P < .0008), HLA-haploidentical (58% versus 38%; P = .01), or UCB (67% versus 48%; P = .004) grafts. Our results demonstrate the utility of the augmented comorbidity/age index as a valid prognostic tool among recipients of allogeneic HCT from alternative graft sources.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".