Impact of Telemedicine on Patient-Centered Outcomes in Pediatric Critical Care: A Systematic Review
Bibliographic record
Abstract
Background: Pediatric intensive care units (ICUs) face shortages of intensivists, posing challenges in delivering specialized care, especially in underserved regions. While studies on telecritical care in the adult ICU have demonstrated decreased complications and mortality, research on telemedicine in the pediatric ICU setting remains limited. This systematic review evaluates the safety and efficacy of audiovisual telemedicine in pediatric ICUs, assessing patient-centered outcomes when compared to in-person intensivist care. Methods: Two reviewers independently assessed studies from PubMed, MEDLINE (Ovid), Global Health, and EMBASE on the pediatric population in the ICU setting that were provided care by intensivists via telemedicine. Studies without a comparison group of in-person intensivists were excluded. Selected studies were graded using the Newcastle–Ottawa scale and the Levels of Evidence Rating Scale for Therapeutic Studies. Results: Of the 2419 articles identified, 7 met the inclusion criteria. Strong evidence suggested that telemedicine increases access to intensive care. Moderate evidence demonstrated that telemedicine facilitates real-time clinical decision-making, reliable remote clinical assessments, improved ICU process measures (i.e., days on a ventilator, days on antibiotics), and decreased length of stay. Weaker evidence supported that telemedicine decreases complications and mortality. Conclusions: Telemedicine may serve as a promising solution to pediatric ICUs with limited intensivist coverage, particularly in low-resource rural and international settings.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".