Prognostic implication of pre‐transplant FEV<sub>1</sub> on long‐term outcomes following allogeneic hematopoietic stem cell transplantation
Bibliographic record
Abstract
Abstract Background Pre‐transplant pulmonary function testing (PFT) is essential before allogeneic hematopoietic stem cell transplant (HCT), yet the optimal cutoff value for affecting transplant outcomes remains poorly defined. Study Design Retrospective analysis of pre‐HCT PFT data from 605 consecutive patients at the Princess Margaret Cancer Centre between January 1, 2004 and December 31, 2013 used binary recursive partitioning to identify cutoff values for overall survival (OS) as an endpoint of transplant outcomes. These values were compared to HCT comorbidity index (HCT‐CI) FEV1 cutoffs for OS, cumulative incidence of relapse and non‐relapse mortality. Results FEV1 ≥ 81% was the identified cutoff point. The OS rate at 3 years showed 49.8% (FEV1 ≥ 81%) vs. 36.6% (<81%, p < .001). For HCT‐CI cutoffs, the OS rate at 3 years for FEV1 ≥ 80%, 66%–80% and ≤65% were 49.0%, 38.1% and 37.6% (p = .011), respectively. Multivariate analysis confirmed that FEV1 ≥ 81% predicted reduced mortality (HR 0.682, p = .001). Subgroup analysis showed both FEV1 ≥ 81% and FEV1 by HCT‐CI cutoffs may stratify patients according to OS and NRM risk in subgroups receiving myeloablative, but not reduced intensity conditioning. Conclusion FEV1 ≥ 81% can predict OS and NRM in our cohort and is potentially simpler when risk stratifying patients undergoing allogeneic HCT, particularly those receiving myeloablative conditioning.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".