A study of the perceived risks, benefits and barriers to the use of SDD in adult critical care units (The SuDDICU study)
Bibliographic record
Abstract
BACKGROUND: Hospital acquired infections are a major cause of morbidity and mortality and markedly increased health care costs. Critically ill patients who require management in an Intensive Care Unit are particularly susceptible to these infections which are associated with a very high mortality. Selective decontamination of the digestive tract (SDD) may reduce these infections and improve mortality but it has not been widely adopted into practice. We aim to 1. Clarify reasons why clinicians have avoided implementing SDD into clinical practice despite the current best-evidence 2. Describe barriers to SDD implementation and 3. Identify what further evidence is required before full scale clinical implementation would be considered appropriate and feasible. METHODS: We have developed an international 'multi-lens' approach to investigate SDD from several perspectives. In case studies we will identify accounts of implementation of SDD in practice, in terms of the behaviours performed by the full range of individual clinicians, accounts of how SDD was first introduced into the Unit and specific content that may be used to populate the content of behaviour change techniques to be used in an implementation intervention and procedures to consider in order to deliver an implementation trial. In a 4 round Delphi study we will identify the range of stakeholders' beliefs, views and perceived barriers relating to the use of SDD. We will generate hypotheses about key beliefs about SDD and will inform the feasibility of any future randomised controlled trial. In large-scale nationwide postal questionnaire surveys of the state of current practice we will identify the factors predicting acceptability of an effectiveness or implementation trial using, and informed by, the theoretical domains structure. In semi-structured interviews with active international clinical trialists we will assess the feasibility of a randomised controlled trial and identify challenges and barriers to undertaking research in the field of SDD research. DISCUSSION: We believe these methods will allow us to determine whether clinical implementation trials or further large effectiveness trials are required before full scale implementation into clinical practice.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".