More than Just a Simple Swish and Spit: Implementation of Oral Care Best Practice in Clinical Neurosciences
Bibliographic record
Abstract
Suboptimal oral care is well documented in the literature and is linked to increased nosocomial pneumonia rates and prolonged hospitalization, negatively affecting patients' quality of life (Terezakis et al. 2011). A standardized approach to oral care can change these adverse outcomes. This project used best practice guidelines and evidence in the literature to guide the development of oral care best practice within an acute care inpatient unit. Based on the work of the interprofessional Clinical Neurological Sciences (CNS) Continuous Quality Improvement (CQI) Council at London Health Sciences Centre-University Hospital (LHSC-UH), an oral care policy and bedside assessment tool were implemented in line with Stroke Best Practice Recommendations (Heart and Stroke Foundation of Canada 2010). A validated, reliable and feasible oral health assessment tool (OHAT) was selected for implementation, and is now completed on every patient within 24 hours of admission to the CNS inpatient unit. Favourable outcomes to date include improved accessibility of oral health supplies, including regular and suction toothbrushes, toothpaste and bite blocks. Post-implementation audits indicate increased frequency and quality of oral care. This review provides a synopsis of how oral care best practice was implemented in an acute care neurology/neurosurgery setting.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".