[Summary of the best evidence for diaphragmatic function training in mechanically ventilated patients].
Bibliographic record
Abstract
OBJECTIVE: To summarize the best available evidence regarding diaphragmatic function training in mechanically ventilated patients and to establish a foundation for clinical practice. METHODS: Systematic searches were conducted in databases and official websites including UpToDate, the National Guideline Clearinghouse (NGC), the Registered Nurses' Association of Ontario (RNAO), the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Web of Science, CNKI, Wanfang Data, VIP, and Yimaitong. The search period covered from the inception of each database to December 31, 2024. The types of evidence included guideline, clinical decision, expert consensus, systematic review, Meta-analysis, and randomized controlled trial (RCT). Two researchers conducted the literature search, study selection, quality assessment and evidence extraction and synthesis independently. RESULTS: A total of 16 articles were included, consisting of 1 guideline, 1 clinical decision, 2 expert consensuses, 4 systematic reviews, 3 Meta-analyses, and 5 RCTs. Nineteen pieces of evidence were ultimately categorized into 7 dimensions, including implementation team, intervention timing, training assessment, training methods, management of ICU-acquired weakness (ICU-AW), monitoring and safety, and outcome evaluation. CONCLUSIONS: This summary of best evidence for diaphragmatic function training in mechanically ventilated patients is scientifically rigorous and comprehensive, offering a valuable reference for guiding clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".