Incidence and Mortality of Children Receiving Home Mechanical Ventilation
Bibliographic record
Abstract
OBJECTIVES: The incidence, as well as the predictors of mortality, for children receiving home mechanical ventilation (HMV) using population-based data in Canada is a current knowledge gap. Our objectives were to describe HMV incidence and mortality rates, and associations of demographic and clinical variables on mortality. METHODS: Using Ontario health and demographic administrative databases, we conducted a retrospective cohort study (April 1, 2003-March 31, 2017) of children aged 0 to 17 years receiving HMV via invasive mechanical ventilation and noninvasive ventilation. We identified children with complex chronic conditions. We used data from Census Canada to calculate incidence rates and Cox proportional hazards modeling to assess for predictors of mortality. RESULTS: We identified 906 children with a mean (SD) crude incidence rate of 2.4 (0.6) per 100 000 for pediatric HMV approvals that increased by 37% over the 14-year study period. Compared with children who were invasively ventilated, we found mortality was associated with noninvasive ventilation (adjusted hazard ratio [aHR], 1.9; 95% confidence interval [CI], 1.3-2.8). Mortality was highest in children from families in the lowest income quintile (aHR, 2.5; 95% CI, 1.5-4.0), those with neurologic impairment complex chronic conditions (aHR, 2.9; 95% CI, 1.4-6.4), those aged 11 to 17 years at HMV initiation (aHR, 1.5; 95% CI, 1.1-2.0), and those with higher health care costs in the 1 year before HMV initiation (aHR, 1.5; 95% CI, 1.3-1.7). CONCLUSIONS: The incidence of children receiving HMV increased substantially over the 14-year period. Demographic variables associated with increased mortality were identified, suggesting areas requiring greater attention for care providers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".