Big Data for Little People: Adherence to Non-invasive Ventilation in a Population-based Cohort of Children
Bibliographic record
Abstract
Abstract RATIONALE Non-invasive ventilation (NIV) is increasingly prescribed for pediatric home mechanical ventilation. However, NIV's effectiveness depends on adherence, which has not been evaluated at a population-based level in pediatric studies. To address this gap, we aimed to evaluate NIV adherence in children using data from Ontario's Ventilator Equipment Pool, a provincial service supporting thousands with respiratory equipment. METHODS We conducted a retrospective longitudinal cohort study using linked ventilator adherence and health administrative data from pediatric patients in Ontario, Canada. We included insured children under 18 years, with remote telemonitoring initiated between June 2020 and December 2022. Patients were followed until June 2023 to ensure at least six months of data. The first available NIV download date was the index date. Children without valid monthly NIV data were excluded. Descriptive statistics characterized the cohort and adherence measures: (i) primary: proportion of children using NIV ≥4 hours per a 24-hour period (i.e., per day); and (ii) secondary: proportion with any NIV use per day; daily usage hours on days used; and number of months/days per month with no valid data. RESULTS We included 237 children, analyzing 3,687 valid person-months (median: 13 months; IQR: 8-24). Mean (SD) age was 12.9 (4.1) years, 145 (61.2%) were male, 25 (10.5%) lived in rural areas, and 45 (19.0%) were from the lowest income quintile neighborhoods. Most (190; 80.2%) had at least one complex chronic condition, with neuromuscular conditions being the most prevalent (122; 51.5%). Additionally, 128 (54.0%) had an emergency department visit/hospitalization in the past year. Figure 1 presents the monthly distribution of NIV adherence measures. The monthly mean (SD) proportion of children using NIV ≥4 hours per day was 58.5% (31.6), while 68.0% (SD: 26.4) had any NIV use. For days when NIV was used, the mean (SD) daily usage was 7.5 (3.2) hours. Missing data was common, with 162 (68.4%) having at least one month with no data, averaging 4.4 (SD: 5.0) days per month. CONCLUSIONS This population-based study describes pediatric NIV adherence, with many children not consistently meeting the recommended ≥4 hours per night, similar to adult patterns. The comparable proportions of any use and ≥4 hours suggest that many children who used NIV did so as prescribed. Our findings underscore the need for improved monitoring and support to address true data gaps versus data gaps due to hospitalization or failure to connect the ventilator, which may lead to misinterpretation of adherence.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".