Respiratory management strategies for Duchenne muscular dystrophy: practice variation amongst canadian sub‐specialists
Bibliographic record
Abstract
PURPOSE: Respiratory management of Duchenne muscular dystrophy (DMD) is not well studied and may vary across centers and practitioners. Our objective was to describe and compare the respiratory management practices of Canadian Pediatric Respirologists and Neuromuscular specialists for children with DMD. METHODS: A web-based survey was sent to all 56 practicing Canadian Pediatric Respirologists and to all 24 members of the Canadian Pediatric Neuromuscular Group (CPNG) who follow children with neuromuscular diseases. The survey included 28 questions about timing and indications for respiratory consultation, sleep disordered breathing (SDB) assessments, and treatments. RESULTS: Thirty eight (68%) pediatric respirologists and 17 (71%) CPNG members responded. Respirologists provide initial consultation after a patient's first admission to hospital with respiratory complications (14/38, 37%) and when symptoms of SDB are present (14/38, 37%). Half of the CPNG members request initial Respirology consultation at the time of DMD diagnosis. Both groups request routine pulmonary function tests. Ninety-six percent of respirologists use maximal inspiratory (MIP) and expiratory pressures (MEP) to assess respiratory muscle strength, whereas 82% of CPNG members additionally use peak cough flow. Assessment for SDB is requested by both groups when pulmonary function is abnormal or patients are symptomatic. Respirologists favor polysomnography, whereas CPNG members use overnight pulse oximetry. Nocturnal non-invasive ventilation and lung volume recruitment (LVR) are used in a minority of patients. CONCLUSIONS: Respirologists and CPNG members provide similar respiratory management of DMD patients, but differ in timing of consultation and choice of tests for pulmonary function and SDB. Canadian practices differ from the American Thoracic Society and Centre for Disease Control guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".