Outcome of non-invasive positive pressure ventilation in paediatric neuromuscular disease
Bibliographic record
Abstract
BACKGROUND: Non-invasive positive pressure ventilation (NPPV) has a beneficial effect on nocturnal hypoventilation and hospitalisation rates in adults with static or slowly progressive neuromuscular disease and respiratory failure. Its role in children affected with similar disease processes, however, remains unclear. AIMS: To investigate the impact of NPPV on hospitalisations and sleep related respiratory parameters in children with neuromuscular disease. METHODS: Fifteen children (mean age 11.7, range 3.4-17.8 years) diagnosed with neuromuscular disease who had been started on nocturnal NPPV and had at least one year of follow up since the initiation of such therapy were studied. Patients served as their own controls and comparison was made of the years preceding and following the initiation of NPPV. RESULTS: Children spent 85% fewer days in hospital (mean pre-NPPV 48.0 days, mean post-NPPV 7.0 days) and 68% less days in intensive care after initiation of NPPV (mean pre-NPPV 12.0 days, mean post-NPPV 3.9 days). Sleep study parameters including number of desaturations, apnoea-hypopnoea index and transcutaneous pCO2 levels improved after initiation of NPPV. CONCLUSIONS: NPPV can decrease hospitalisations for children with neuromuscular disease and improves sleep related respiratory parameters. A prospective study is now needed to further delineate the role of NPPV in this population of children.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".