Noninvasive Ventilation: Education and Training. A Narrative Analysis and an International Consensus Document
Bibliographic record
Abstract
Noninvasive ventilation (NIV) is an increasingly used method of respiratory support. The use of NIV is expanding over the time and if properly applied, it can save patients' lives and improve long-term prognosis. However, both knowledge and skills of its proper use as life support are paramount. This systematic review aimed to assess the importance of NIV education and training. Literature search was conducted (MEDLINE: 1990 to June, 2018) to identify randomized controlled studies and systematic reviews with the results analyzed by a team of experts across the world through e-mail based communications. Clinical trials examining the impact of education and training in NIV as the primary objective was not found. A few studies with indirect evidence, a simulation-based training study, and narrative reviews were identified. Currently organized training in NIV is implemented only in a few developed countries. Due to a lack of high-grade experimental evidence, an international consensus on NIV education and training based on opinions from 64 experts across the twenty-one different countries of the world was formulated. Education and training have the potential to increase knowledge and skills of the clinical staff who deliver medical care using NIV. There is a genuine need to develop structured, organized NIV education and training programs, especially for the developing countries.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".