A critical review of daily sedation interruption in the intensive care unit
Bibliographic record
Abstract
BACKGROUND: Daily sedation interruption (DSI) has been proposed as a method of improving sedation management of critically ill patients by reducing the adverse effects of continuous sedation infusions. AIM: To critique the research regarding daily sedation interruption, to inform education, research and practice in this area of intensive care practice. DESIGN: Literature review. METHOD: Medline, CINAHL and Web of Science were searched for relevant key terms. Eight research-based studies, published in the English language between 1995-December 2006 and three conference abstracts were retrieved. RESULTS: Of the eight articles and three conference abstracts reviewed, five originated from one intensive care unit (ICU) in the USA. The research indicates that DSI reduces ventilation time, length of stay in ICU, complications of critical illness, incidence of post-traumatic stress disorder and is reportedly used by 15-62% of ICU clinicians in Australia, Europe, USA and Canada. CONCLUSIONS: DSI improves patients' physiological and psychological outcomes when compared with routine sedation management. However, research relating to these findings has methodological limitations, such as the use of homogenous samples, single-centre trials and retrospective design, thus limiting their generalisability. RELEVANCE TO CLINICAL PRACTICE: DSI may provide clinicians with a simple, cost-effective method of reducing some adverse effects of sedation on ICU patients. However, the evidence supporting DSI is limited and cannot be generalised to heterogeneous ICU populations internationally. More robust research is required to assess the potential impact of DSI on the physical and mental health of ICU survivors.
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".