Assessing the potential for virtualizable care in the pediatric emergency department
Bibliographic record
Abstract
INTRODUCTION: There is increasing interest for patient-to-provider telemedicine in pediatric acute care. The suitability of telemedicine (virtualizability) for visits in this setting has not been formally assessed. We estimated the proportion of in-person pediatric emergency department (PED) visits that were potentially virtualizable, and identified factors associated with virtualizable care. METHODS: This was a retrospective analysis of in-person visits at the PED of a Canadian tertiary pediatric hospital (02/2018-12/2019). Three definitions of virtualizable care were developed: (1) a definition based on "resource use" classifying visits as virtualizable if they resulted in a home discharge, no diagnostic testing, and no return visit within 72 h; (2) a "diagnostic definition" based on primary ED diagnosis; and (3) a stringent "combined definition" by which visits were classified as virtualizable if they met both the resource use and diagnostic definitions. Multivariable logistic regression was used to identify factors associated with telemedicine suitability. RESULTS: There were 130,535 eligible visits from 80,727 individual patients during the study period. Using the most stringent combined definition of telemedicine suitability, 37.9% (95% confidence interval (CI) 37.6%-38.2%) of in-person visits were virtualizable. Overnight visits (adjusted odds ratio (aOR) 1.16-1.37), non-Canadian citizenship (aOR 1.10-1.18), ethnocultural vulnerability (aOR 1.14-1.22), and a consultation for head trauma (aOR 3.50-4.60) were associated with higher telemedicine suitability across definitions. DISCUSSION: There is a high potential for patient-to-provider telemedicine in the PED setting. Local patient and visit-level characteristics must be considered in the design of safe and inclusive telemedicine models for pediatric acute care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".