The use of telemedicine in the PICU: A systematic review and meta-analysis
Bibliographic record
Abstract
The use of telemedicine in ICUs has grown and is becoming increasingly recognized. However, its impact on PICUs remains unclear. This systematic review and meta-analysis aimed to evaluate whether telemedicine in the PICU has the potential to improve clinical and non-clinical outcomes. PubMed, Scopus, LILACS, and CINAHL electronic databases were searched to identify studies that assessed the impact of telemedicine on clinical outcomes, with no publication date restrictions. The reference lists of the selected articles were hand-searched for additional studies that had not been identified by the initial electronic search. Studies were included if they had a cohort design, used telemedicine, were conducted in PICUs or specialized PICUs, and were published in Portuguese, English, or Spanish. Two groups of reviewers independently screened titles and abstracts for inclusion. The same group of reviewers independently assessed the full-text articles for eligibility and extracted the following information: telecommunication method, intervention characteristics, patient characteristics, sample size, and main results. Studies were meta-analyzed using a random-effects model to estimate the pooled prevalence of PICU mortality and length of PICU stay. Risk of bias was assessed using the Newcastle-Ottawa Scale. Of 2703 studies initially identified, 2226 had their titles and abstracts screened. Of these, 53 were selected for full-text reading, of which 10 were included and analyzed. The main results of interest were length of PICU stay, number of deaths or mortality rate, and satisfaction of health professionals and family members. The results of meta-analysis show that the mortality rate reduced by 34% with an increase of the length of PICU stay in the PICUs with the use of telemedicine. Family members and health professionals were satisfied with the use of telemedicine. Telemedicine has the potential to improve PICU outcomes, such as mortality rate and family and staff satisfaction. However, it extended length of PICU stay in the studies included in this systematic review.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.046 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".