Professional oral health care prevents mouth-lung infection in long-term care homes: a systematic review.
Bibliographic record
Abstract
Background: Nursing home-acquired pneumonia (NHAP) is the leading cause of mortality among residents in long-term care (LTC) homes. Aspiration pneumonia (AP) is one cause of NHAP. Professional oral health care (POHC) and daily mouth care can be effective in decreasing AP risk. Aim: To identify, appraise, synthesize, analyze, and interpret results on the effectiveness of onsite POHC interventions/programs delivered to LTC home residents in reducing oral disease and NHAP. To summarize the findings and provide recommendations for clinical work and future research. Methods: The PICO question addressed was, "In LTC home residents with oral health needs (P), is onsite POHC (I), compared to usual care (C), clinically effective in reducing dental disease and pneumonia/AP (O)?" Databases searched were PubMed, EMBASE (Ovid), CINAHL (Ebsco), Cochrane Library (Wiley), Web of Science, and the databases of the Centre for Reviews and Dissemination. Included were randomized controlled trials (RCTs), non-RCTs, and cross-sectional studies. PRISMA guidelines were followed and GRADE was used to assess the quality of studies. Results: Thirteen clinical effectiveness studies were included: 10 RCTs, 1 non-RCT, and 2 cross-sectional studies. Discussion: Better oral health and respiratory infection outcomes were found in the experimental groups who received an onsite POHC intervention compared to the control groups. Conclusion: There is moderate-to-strong evidence that onsite POHC in LTC homes, provided mostly by dental hygienists, is effective in preventing bacterial mouth infection, pneumonia, and AP.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".