Oral care associated with a stay in an intensive care unit (ICU): A systematic review of clinical practice guidelines and scientific statements
Bibliographic record
Abstract
The aim of the present systematic review was to critically evaluate the recommendations from evidencebased clinical practice guidelines (CPG) and scientific statements (SS), as well as expert consensus, related to the management of oral complications in intensive care unit (ICU) patients.A search was made in the PubMed, Scopus, Ovid/Cochrane, and LILACS databases, following the CPG identification filters from the Canadian Agency for Drugs and Technologies in Health (CADTH). Both scientific repositories and document references were incorporated as well. The critical assessment was performed by means of the AGREE-II instrument (an ideal scenario) for CPG and SS, and using the AGREEREX instrument for recommendations (ideal and local scenarios).A total of 13 related recommendations from 4 SS were included. The mean score in AGREE-II was 58.25. The mean AGREE-REX scores were 45.82 and 39.07 for the ideal and local scenarios, respectively. The included recommendations focused on the oral care assessment, and the development of prevention and execution tools with regard to respiratory infections.There is a lack of CPG following a rigorous methodology that would incorporate recommendations for oral care in ICU. Dentists are responsible for the development and improvement of recommendations from CPG and/or SS to mitigate oral complications in ICU patients.
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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 | 0.023 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".