Assessment of healthcare consumption and asthma control after implementation of remote monitoring in long-term multicentre paediatric asthma care.
Bibliographic record
Abstract
Introduction: Real-life, long-term and large-scale studies on remote monitoring (RM) in respiratory care are needed to determine its true impact on daily practice. Aims and objectives: We aimed to determine the effect of RM on healthcare consumption in paediatric asthma in four Dutch hospitals. Methods: Cohort study using RM and healthcare consumption data of children with asthma in four Dutch hospitals from 2017 until 2023. Patients aged 6-18 years with at least two years follow-up were included. Differences in number of outpatient visits between patients with RM and regular care were assessed. Incidence rate ratios (IRR) and Number Needed to Treat (NNT) were calculated for any emergency visits and hospitalisations. Interrupted time series analysis (ITSA) was used to evaluate the change in number of outpatient visits after initiation of RM over time. We assessed asthma control after RM initiation over time. Results: We included 1.278 children of which 687 (53.8%) used RM at some time during the study period. Number of outpatient visits was lower in the RM group (∆Medians 0.62 visits/year, p = <0.001). The IRR for emergency visits was 0.55 (95%CI 0.44, 0.68; NNT 24) and for hospitalizations 0.42 (95%CI 0.30, 0.59; NNT 35) in favour of RM. ITSA showed a significant reduction in outpatients visits per quarter per 100 patients (-0.90; 95%CI -1.79, -0.10). The proportion of participants with controlled asthma increased from 68.6% to 90.1% 43 months after introduction of RM. Conclusions: These findings further confirm the impact of RM in routine paediatric asthma care by reducing healthcare consumption with high rates of well-controlled asthma.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".