Application of treatment bundles reduces days on mechanical ventilation in critically ill patients
Bibliographic record
Abstract
Reduction of time on the ventilator is a key concept to avoid complications. Recommendations include semirecumbent positioning (SRP) [ 1 ], low tidal volume ventilation (TV = 6 ml/kg) [ 2 ], prophylaxis for stress ulcer (SUP) [ 3 ], and deep vein thrombosis (DVTP) [ 4 ]. The goal of this study was to investigate whether staff training about these treatments decreases days on ventilation. All patients of a 50-bed ICU with mechanical ventilation >24 hours were included. From June 2005 to September 2005 (Audit I), patients were examined daily for SRP >30°, low tidal volume ventilation, DVTP, and SUP by an independent task force. Afterwards, nurses and physicians were trained for the monitored treatments. Audit II was then performed from March 2006 to June 2006. One hundred and thirty-three patients (1,389 ventilator-days) were included in Audit I, 141 patients (1,002 ventilator-days) in Audit II. Data are expressed as the median (interquartile range) or percentage of implementation per ventilator-days (Table 1 ). On average, low tidal volume ventilation was adopted. DVTP and SUP were well implemented without training. There was no effect on frequency of pneumonia, ICU length of stay, or survival. SRP could be successfully improved by staff training. Enhanced implementation was associated with reduction in days on ventilation.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".