Oral Care in Acute Stroke
Bibliographic record
Abstract
Purpose: Stroke impacts independent activities, particularly personal care such as oral hygiene. Existing guidelines lack details about how to provide effective oral care. This study explores the frequency of oral care based on staff adherence to oral care policies. Method: As part of a quality improvement initiative, we conducted a retrospective chart review of 30 consecutive stroke admissions to an acute care hospital. Patients with primary intracerebral hemorrhage or ischemic stroke were eligible. Sources of information included a decision support database and an electronic chart review. Data collection included length of hospitalization, stroke type, presence of infections, oral mechanism exam, dysphagia evaluations, and daily personal care provision by nursing staff (e.g., oral care and bathing). Results: Twenty-seven patients met the inclusion criteria. They had a mean age of 74 years, and 52% were men. Most had supratentorial stroke (86%) with comparable frequencies of right (48%) and left (52%) hemisphere lesions. In over half of the cases, provision of oral care was not documented at any point during the patients' hospitalization (Mdn= 128 hr). Pericare and bathing were about twice and 4 times more frequent than oral care, respectively. Conclusions: Oral care after stroke is challenging. Existing oral care recommendations from stroke guidelines lack sufficient detail and warrant reconsideration for optimal and routine implementation particularly in acute settings. Education around oral care and associated protocols are necessary to advance oral care practices and improve stroke recovery.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".