Remote Presence Robotic Technology Reduces Need for Pediatric Interfacility Transportation from an Isolated Northern Community
Bibliographic record
Abstract
BACKGROUND: Providing acutely ill children in isolated communities access to specialized care is challenging. This study aimed to evaluate remote presence robotic technology (RPRT) for enhancing pediatric remote assessments, expediting initiation of treatment, refining triaging, and reducing the need for transport. METHODS: We conducted a pilot prospective observational study at a primary/urgent care clinic in an isolated northern community. Participants (n = 38) were acutely ill children <17 years presenting to the clinic, whom local healthcare professionals had considered for interfacility transportation (IFT). Participants were assessed and managed by a tertiary center pediatric intensivist through a remote presence robot. The intensivist triaged participants to either remain at the clinic or be transported to regional/tertiary care. Controls from a pre-existing local transport database were matched using propensity scoring. The primary outcome was the number of IFTs among participants versus controls. RESULTS: Fourteen of 38 (37%) participants required transport, whereas all controls were transported (p < 0.0001). Six of 14 (43%) transported participants were triaged to a nearby regional hospital, while no controls were regionalized (p = 0.0001). All participants who remained at the clinic stayed <24 h, and were matched to controls who stayed 4.9 days in tertiary care (p < 0.001). There was no statistically significant difference in hospital length of stay between transported participants and controls (6.0 vs. 5.7 days). CONCLUSIONS: RPRT reduced the need for specialized pediatric IFT, while enabling regionalization when appropriate. This study may have implications for the broader implementation of RPRT, while reducing costs to the healthcare system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".