The impact of hospitalization on oral health: a systematic review
Bibliographic record
Abstract
BACKGROUND: Poor oral health of hospitalized patients is associated with an increased risk of hospital-acquired infections and reduced life quality. OBJECTIVES: To systematically review the evidence on oral health changes during hospitalization. DATA SOURCES: Cochrane library, Medline, OldMedline, Embase and CINAHL without language restrictions. STUDY ELIGIBILITY CRITERIA: Observational longitudinal studies. DATA APPRAISAL AND SYNTHESIS METHODS: Two independent reviewers screened studies for inclusion, assessed the risk of bias and extracted data. Risk of bias was assessed using the Newcastle-Ottawa assessment scale. A narrative synthesis was conducted. RESULTS: Five before and after studies were included. The data suggest a deterioration in oral health following hospitalization with an increase in dental plaque accumulation and gingival inflammation and a deterioration in mucosal health. LIMITATIONS: While before and after studies are at a general risk of bias, other specific study characteristics were judged to have a low risk of bias. However, methodological issues such as unvalidated outcome measures and the lack of assessor training limit the strength of the evidence. CONCLUSION: Hospitalization is associated with a deterioration in oral health, particularly in intubated 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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".