[Summary of the best evidence for the use of mechanical insufflation-exsufflation technique in invasive mechanical ventilation patients].
Bibliographic record
Abstract
OBJECTIVE: To search and evaluate the literatures on the application of mechanical insufflation-exsufflation technique (MI-E) in patients with invasive mechanical ventilation in China and abroad, and to summarize the best evidence to provide evidence-based basis for clinical practice. METHODS: The literatures related to the use of MI-E technique in invasive mechanical ventilation patients were searched from the establishment of the database to April 1, 2022 in BMJ Best Practice, UpToDate Clinical Advisor, Scottish Intercollegiate Guidelines Network (SIGN), Guidelines International Network (GIN), National Institute for Health and Care Excellence (NICE), National Guideline Clearinghouse (NGC), Registered Nurses' Association of Ontario (RNAO), medlive, Cochrane Library, Joana Briggs Instiute, Web of Science, PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), SinoMed, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database, and Website of American Association for Respiratory Care, including guideline, expert consensus, clinical decision, evidence summary, systematic review and randomized controlled trial. The JBI Center for Evidence-Based Health Care Expert Consensus Evaluation Criteria (2016) was used to evaluate the quality of the included systematic reviews; the JBI Center for Evidence-Based Health Care Evaluation Criteria for Randomized Controlled Trials was used to evaluate the quality of the included randomized controlled trial. Two researchers independently evaluated the quality of literature, and extracted and summarized the evidence based on professional judgment. RESULTS: A total of 7 literatures were enrolled, including 3 systematic reviews and 4 randomized controlled trials. After quality evaluation, 7 articles were all enrolled. Thirteen best evidences were formed from four aspects of indications, contraindications, parameter settings, and attention. CONCLUSIONS: The study summarizes the best evidence for the application of MI-E technique in invasive mechanical ventilation patients. It is recommended that medical staff undergo professional training, combined with their professional judgment as well as the patient's clinical specific conditions and willingness, and accurately apply MI-E technology to invasive mechanical ventilation patients.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".