Overview of oral health status and associated risk factors in maritime settings: An updated systematic review
Bibliographic record
Abstract
OBJECTIVES: The aim of this study is to provide an updated overview of the oral health status and associated risk factors in maritime settings. METHODS: We systematically searched PubMed, Ovid Embase, Web of Science, CINAHL and SCOPUS from January 2010 to April 2023. Two independent reviewers extracted the data. The quality of included studies was assessed using relevant assessment tools. RESULTS: A total of 260 records were found in the initial search; 24 articles met the inclusion criteria. Most studies had descriptive design, and only two randomized controlled trials were found. The main oral health issues noted are oral cancer, dental caries, periodontal diseases, oral mucosal lesions, and dental emergency. Male seafarers have higher risk of oral cancers in the tongue, lips, and oral cavity while oral mucosal lesions are more prevalent among fishermen. CONCLUSIONS: Dental caries and periodontal diseases are prevalent in both seafarers and fishermen. The consumption of tobacco, alcohol, fermentable carbohydrate, and poor oral hygiene are risk factors that affect the oral health status at sea. The occurrence of oral diseases in maritime setting requires more attention of researchers and authorities to develop strategies to tackle these issues. TRIAL REGISTRATION: Systematic review registration number in PROSPERO: CRD42020168692.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".