Monitoring for paediatric pulmonary graft <i>versus</i> host disease with multiple breath washout
Bibliographic record
Abstract
Background Current diagnostic tests for pulmonary chronic graft versus host disease (p-cGvHD) are either invasive or challenging for children to perform. Multiple breath washout (MBW) has been proposed as a feasible and sensitive tool for the diagnosis of p-cGvHD. In this study, we aimed to determine the feasibility and sensitivity of MBW to monitor for p-cGvHD in children after haematopoietic stem cell transplantation (HSCT). Methods This was a prospective, single-centre cohort study, recruiting children >3 years of age undergoing HSCT between February 2019 and January 2024. MBW and spirometry were performed at regular follow-up visits out to 2 years post-HSCT. Results 46 children were included in the longitudinal analysis, five (10.9%) of whom developed p-cGvHD. MBW success rate was 93.9% (229 out of 244 visits), compared to 78.3% for spirometry (191 out of 244 visits). In children who developed p-cGvHD, compared to those who did not, peak lung clearance index (LCI) was higher (12.6 versus 6.8, p=0.0003), nadir forced expiratory volume in 1 s z-score (zFEV 1 ) was lower (−3.5 versus −0.7, p=0.005) and nadir zFEV 1 :forced vital capacity (FVC) was lower (−2.4 versus −0.3, p=0.01). The sensitivity and specificity for p-cGvHD were 100%/97.6% for peak LCI, 100%/71.1% for nadir FEV 1 and 75.0%/94.7% for nadir FEV 1 :FVC. In those who do not develop p-cGvHD, longitudinal LCI remained normal or mildly abnormal (LCI <8.0) in 98.1% of visits, compared to FEV 1 (z > −1.9) in 90.2% and zFEV 1 :FVC in 95.5% of visits. Conclusions MBW is longitudinally feasible in a post-HSCT population as young as 3 years of age. LCI is highly sensitive and specific for p-cGvHD and may help to distinguish different phenotypes of post-HSCT lung disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".