Pre‐transplant lung function is predictive of survival following pediatric bone marrow transplantation
Bibliographic record
Abstract
BACKGROUND: Pulmonary toxicity is well described in recipients of bone marrow transplants (BMT), and accounts for a sizeable proportion of post-transplant mortality. The majority of the data on post-transplant pulmonary function is from adults, although several small pediatric case series have been described. In adults, pre-transplant lung function has been predictive of post-transplant respiratory failure and mortality. This use of pulmonary function testing, that is, for pre-transplant risk counseling, is novel but has never been applied to pediatric patients. We hypothesized that in children, as in adults, pre-transplant pulmonary function would also be predictive of outcome post-transplantation morbidity. PROCEDURE: Retrospective database analysis of pulmonary function tests of patients undergoing first myeloablative BMT at two large children's hospitals. RESULTS: Two hundred seventy-three subjects had at least one pre-transplant PFT, and 317 subjects had at least one post-transplant PFT available for analysis. While the majority of patients had normal or mildly reduced pre-transplant flows and lung volume, 25% had moderately or severely reduced diffusion. All lung function parameters decreased post-transplant with a slow improvement over ensuing years. The Lung Function Score, a combined measurement of FEV(1) and DLCO, was highly associated with post-transplant survival. Hazard ratios for mortality (compared to the best LFS) ranged from 1.654 to 2.454. CONCLUSIONS: Lung function prior to bone marrow transplant, especially diffusing capacity, is frequently abnormal. Lung function frequently decreases shortly post-transplant and tends to improve over time, but frequently remains abnormal even years after transplant. Post-transplant survival is related to pre-transplant lung function.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".