Free-breathing MRI for monitoring ventilation changes following antibiotic treatment of pulmonary exacerbations in paediatric cystic fibrosis
Bibliographic record
Abstract
Treatment response in Cystic Fibrosis (CF) is traditionally monitored using pulmonary function tests (PFTs), such as spirometry. However, PFTs can be insensitive to treatment, particularly in early CF lung disease [1]. Hyperpolarized (HP) 129Xe MRI (Xe-MRI) has been shown to be feasible in children [2], more sensitive to early CF lung disease compared to PFTs [3 and captures improvements in ventilation inhomogeneity in pediatric CF patients receiving intravenous antibiotic treatment for a PEx [4]. However, access to hyperpolarized 129Xe gas is not widely available and Xe-MRI requires subjects to perform an extended breath-hold (10–15 s), which is challenging for very sick children. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr. Munidasa reports grants from Cystic Fibrosis Centre, grants from Natural Sciences and Engineering Research Council of Canada, grants from Canadian Institutes of Health Research, during the conduct of the study. Conflict of interest: Dr. Couch reports that he was supported by a MITACS Elevate Postdoctoral Fellowship, which was funded in part by Siemens Healthcare Limited. Dr. Couch is currently an employee of Siemens Healthcare Limited. This employment began after the conclusion of the study. Conflict of interest: Dr. Rayment reports other from Polarean Inc, outside the submitted work. Conflict of interest: Dr. Voskrebenzev reports In addition, Dr. Voskrebenzev has a patent Method of quantitative magnetic resonance lung imaging Conflict of interest: Dr. Seethamraju reports personal fees from Siemens Medical Solutions, USA Inc., outside the submitted work. Conflict of interest: Dr. Vogel-Claussen reports grants from Siemens Healthineers, during the conduct of the study; grants and personal fees from Boehringer Ingelheim, grants from GSK, grants and personal fees from Astra Zeneca, outside the submitted work; In addition, Dr. Vogel-Claussen has a patent Voskrebenzev, Gutberlet, Vogel-Claussen „Method of quantitative magnetic resonance lung imaging“Nr. EP3107066, US-2016-0367200-A1 22.12.2016 licensed to Siemens Healthineers. Conflict of interest: Dr. Ratjen has nothing to disclose. Conflict of interest: Dr. Santyr reports grants and non-financial support from Siemens Healthineers, grants from Canadian Institutes of Health Research, during the conduct of the study; grants and non-financial support from Siemens Healthineers, outside the submitted work.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".