Respiratory health service utilization of children with neuromuscular disease
Bibliographic record
Abstract
OBJECTIVES: To quantify health service utilization including assessment, monitoring, and treatment of respiratory complications of children with neuromuscular disease (NMD), identifying practice variation and adherence to guideline recommendations at a population level. METHODS: North American population-based cohort study (2003-2015) of children with NMD using hospital diagnostic and physician billing codes within health administrative databases. RESULTS: We identified 18 163 children with NMD. Mean (SD) age was 7.8 (5.6) years with 40% ≤5; 45% were female. Most common diagnoses were cerebral palsy (50%) and spina bifida (16%); 8% had muscular dystrophy. From fiscal years 2003-2014, 15 600 (86%) children went to an emergency department on average 3.5 times every 3 years; 6575 (36%) for respiratory reasons. 8788 (48%) were admitted to hospital with 2190 (12%) for respiratory reasons and 2451 (13%) required intensive care. Respiratory specialist outpatient visits occurred for 2226 (12%) children on average 6.5 visits every 3 years; 723 (4%) had in-hospital respiratory specialist consultation. Pulmonary function testing was conducted in 3194 (18%) children on average 2.4 times every 3 years; sleep studies in 1389 (8%). CONCLUSION: In this population-based study of children with NMD, healthcare utilization for respiratory complications was considerable. Frequency of respiratory specialist consultation, monitoring of respiratory function and sleep disordered breathing was variable but on average reflected professional society recommendations. Children with NMD are frequent ED users suggesting a need to improve community and social supports. We did not detect reduced access to respiratory monitoring or specialist consultation in adolescents transitioning to adult services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".