Diagnosis of Post-Hematopoietic Stem Cell Transplantation Bronchiolitis Obliterans Syndrome in Children: Time for a Rethink?
Bibliographic record
Abstract
Hematopoietic stem cell transplantation (HSCT) is undertaken in children with the aim of curing a range of malignant and nonmalignant conditions. Unfortunately, pulmonary complications, especially bronchiolitis obliterans syndrome (BOS), are significant sources of morbidity and mortality post-HSCT. Currently, criteria developed by a National Institutes of Health (NIH) working group are used to diagnose BOS in children post-HSCT. Unfortunately, during the development of a recent American Thoracic Society (ATS) Clinical Practice Guideline on this topic, it became apparent that the NIH criteria have significant limitations in the pediatric population, leading to late diagnosis of BOS. Specific limitations include use of an outdated pulmonary function testing reference equation, a reliance on spirometry, use of a fixed forced expiratory volume in 1 second (FEV1) threshold, focus on obstructive defects defined by FEV1/vital capacity, and failure to acknowledge that BOS and infection can coexist. In this review, we summarize the evidence regarding the limitations of the current criteria. We also suggest potential evidence-based ideas for improving these criteria. Finally, we highlight a new proposed criteria for post-HSCT BOS in children that were developed by the authors of the recently published ATS clinical practice guideline, along with a pathway forward for improving timely diagnosis of BOS in children post-HSCT.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".