60 Examining the role of virtual health in supporting children hospitalized with severe acute respiratory illness in 2022-2023, CHARLiE and the READAPT-Kids study cohort
Bibliographic record
Abstract
Abstract Background The unprecedented era of the SARS-CoV2 pandemic ushered in virtual care models as tools to promote healthcare access. To support rural and remote providers, a 24/7 on-demand service for virtual paediatric consultation via video conference, called Child Health Advice in ReaL-Time Electronically (CHARLiE), was created. The subsequent surge in paediatric respiratory illnesses during the 2022-2023 viral season resulted in an increased number of paediatric admissions for severe acute respiratory illnesses (SARIs). The impact on paediatric hospitals was profound, but data from rural and remote areas, as well as the role of CHARLiE support in the healthcare journeys of these patients, is unknown. Objectives The objective of this research is to describe the CHARLiE support pathways, clinical presentations, etiologies and outcomes for children from a geographically large and remote healthcare region during hospital admission with severe acute respiratory illnesses (SARI) in the 2022-2023 viral season. Design/Methods The READAPT Kids Study (clinical chaRacteristics and outcomEs of hospitAlized chilDren with Acute resPiratory infecTions) is a multisite retrospective observational cohort of children (ages 0-18 years) hospitalized with SARIs from July 1, 2022, to June 30, 2023. This dataset is a subset of the wider READAPT Kids cohort and includes data from the largest tertiary Paediatric Intensive Care Unit (PICU) in the province, plus three regional hospitals within a single health authority, which spans a geographical area of over 600,000 square kilometers. Cases were identified using ICD-10-CA codes, then manually screened for inclusion criteria. Detailed clinical, demographic, and home postal code data was extracted. In a separate dataset, all contact with the CHARLiE service over the same time period was tracked and linked to the READAPT cohort through unique personal health numbers. Results There were 204 distinct admissions for SARI to 3 regional hospitals within this health authority. The median patient age was 2.12 years, 38% (78/204) were female, many had a chronic comorbid condition and 75% (153/204) had a virus (Table 1). The most common diagnoses were asthma (37%; 75/204) and bronchiolitis (35%; 72/204). The median hospital length of stay was 2 days (1- 4), and 27% (55/204) were admitted for more than 4 days. 10 patients required transfer to the tertiary paediatric hospital and one child died prior to transfer (Table 2). Of the 204 SARI admissions, 57% (116/204) were either admitted to, or lived within a rural or remote community, representing the cohort eligible for CHARLiE support. Of this group, 14% (16/116) had contact with CHARLiE at one point between 72 hours prior to admission and 72 hours after discharge. From the tertiary care hospital cohort, a total of 237 patients were admitted with a SARI directly to the PICU. 65% (155/237) of these direct PICU admissions were transferred from another hospital and 14% (22/155) resided within the corresponding rural and remote health authority described. 54.5% (12/22) of these patients were transferred from community hospitals or nursing stations not captured in the health authority dataset. 36% (8/22) of this rural residing cohort that were admitted to PICU also had contact with CHARLiE during this period. Conclusion CHARLiE supported almost 40% of direct PICU admissions for SARI from within a single, geographically vast, rural and remote health authority region. This patient population has a significant disease burden and length of stay within the regional centres, which highlights the ongoing need for paediatric care services, close to where patients live. Providing virtual, real-time expert paediatric advice, the CHARLiE service is a crucial resource for the rural providers who care for these patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".