Innovative ventilation technologies used in the intensive care unit for adults and children: a scoping review
Bibliographic record
Abstract
Background There is widespread interest in the use of innovative ventilation technologies to improve clinical outcomes across the 13–20 million people each year globally that receive invasive ventilation on an intensive care unit. This scoping review aims to summarise the volume and nature of evidence underpinning the use of 22 innovative ventilation technologies in adults and children. Methods We searched MEDLINE, EMBASE, Cochrane library and other key databases from 2010 to May 2024 for primary studies and systematic reviews that evaluated the use of 22 innovative ventilation technologies in adults and children requiring, or at risk of requiring, invasive ventilation. We defined an innovative ventilation technology as a ventilation approach not currently recommended by clinical guidelines due to lack of or uncertainty of evidence. We summarise findings as evidence maps. Results Our search identified 22,274 records of which we included 851 studies (564 primary studies; 277 systematic reviews; 10 economic evaluation studies). Over 50% of studies focussed on non-invasive respiratory support strategies to reduce the risk of a primary tracheal intubation (n=319, 37%) or re-intubation (n=130, 15%). We identified ten or fewer studies for seven technologies, including phrenic nerve stimulation, artificial intelligence, and ultra-low tidal volume ventilation. Few studies include children (n=128, 15%) or report patient-focussed outcomes (n=19, 2%). Conclusions For many technologies despite being used in clinical practice, the available evidence is currently inadequate to determine its clinical effectiveness, particularly in children. Key technologies need to be evaluated in high-quality multi-centre clinical trials that report patient-focussed outcomes.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".