Role of organisational structure in implementation of sedation protocols: a comparison of Canadian and French ICUs
Bibliographic record
Abstract
PURPOSE: Use of sedation protocols is associated with fewer mechanical ventilation days in critically ill patients. Canadian intensive care units (ICUs) often have a higher nurse-patient ratio and more specialised training of ICU nurses than French ICUs. Considering these differences, the purpose of this study was to compare implementation of sedation protocols as indicated by frequency of sedation assessment and response to levels of sedation between a Canadian and a French ICU. METHODS: This was a retrospective observational study of 30 patients who were mechanically ventilated for at least 24 h in each of two tertiary care ICUs in Vancouver, Canada and Montpellier, France. The authors tabulated all Richmond Agitation-Sedation Scale scores, frequency of score measurement, target scores, frequency and magnitude of scores that were out of target range, and the response to these scores within 1 h of measurement. Practices between the two hospitals were compared using regression modelling, adjusting for patient age, sex, and Acute Physiology and Chronic Health Evaluation (APACHE) II score. RESULTS: Although sedation scores were measured more frequently in the Canadian ICU, there were fewer appropriate adjustments in medications in response to scores that were outside the target range in this ICU than in the French ICU, which had a lower nurse-patient ratio and no specialised training of nurses (OR 0.26 (95% CI 0.13 to 0.50) for scores that were higher than target, and OR 0.14 (95% CI 0.07 to 0.28) for scores that were lower than target). CONCLUSION: Differences in sedation management between these ICUs are likely related to factors other than nurse-patient ratio or specialised training of ICU nurses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".