International Perspectives on the Influence of Structure and Process of Weaning From Mechanical Ventilation
Bibliographic record
Abstract
BACKGROUND: Recently, clinical and research attention has been focused on refining weaning processes to improve outcomes for critically ill patients who require mechanical ventilation. One such process, use of a weaning protocol, has yielded conflicting results, arguably because of the influence of existing context and processes. OBJECTIVE: To compare international data to assess differences in context and processes in intensive care units that could influence weaning. METHODS: Review of existing national data on provision of care for critically ill patients, including structure, staffing, skill mix, education, roles, and responsibilities for weaning in intensive care units of selected countries. RESULTS: Australia, New Zealand, Denmark, Norway, Sweden, and the United Kingdom showed similarities in critical care provision, structure, skill mix, and staffing ratios in intensive care units. Weaning in these countries is generally a collaborative process between nurses and physicians. Notable differences in intensive care units in the United States were the frequent use of an open structure and inclusion of respiratory therapists on the intensive care unit's health care team. Nurses may be excluded from direct management of ventilator weaning in some institutions, as this role is primarily assumed by respiratory therapists guided by medical directives. Availability of critical care beds was highest in the United States and lowest in the United Kingdom. CONCLUSION: Context and processes of care that could influence ventilator weaning outcomes varied considerably across countries. Further quantification of these contextual influences should be considered when translating research findings into local clinical practice and when designing randomized controlled trials.
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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 | 0.035 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".