Use of invasive mechanical ventilation in Australian emergency departments
Bibliographic record
Abstract
OBJECTIVE: There are few published reports describing the use of invasive mechanical ventilation in EDs. We explored the characteristics of patients receiving mechanical ventilation, the ventilator modes and parameters used as well as the duration of ventilation and the nature of ventilator decision-making in Australian ED. METHODS: We conducted a 2 month prospective survey of adult patients who received invasive mechanical ventilation in 24 Australian ED. Data forms were completed by ED staff during the patient's ED presentation. We documented ventilator settings post intubation, after a 1 h stabilization period, and immediately before ED discharge or extubation. The person responsible for selection of ventilator settings was noted at each time point. RESULTS: Data were recorded on 307 patients. Altered mental status (179/307 [58%, 95% CI 53-64]) was the most common indication for mechanical ventilation. Volume-controlled modes were most frequently used at all measured time points; with a median tidal volume of 8 mL/kg. Responsibility for initial selection of ventilator settings was shared between ED physicians (157/307 [51%, 95% CI 46-57]), ED nurses (111/307 [36%, 95% CI 31-42]) and ICU or paramedic staff (9/307 [3%, 95% CI 1-5]) (not recorded 30/307 [10%, 95% CI 6-13]). Ongoing responsibility for titration of ventilation was more commonly that of the ED nurse. CONCLUSION: The application of mechanical ventilation was similar to descriptions reported in the critical care literature both in Australia and internationally. Decision-making responsibilities were shared by ED medical and nursing staff.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".