ELITE ATHLETES AND ORAL HEALTH: A REVIEW
Bibliographic record
Abstract
Background Data on the impact of oral health on sporting performance is missing and the epidemiology of oral diseases in the elite athlete population is poorly understood. Objective To systematically review available evidence to determine the epidemiology of oral diseases in the elite athlete population and the impact of oral health on sporting performance. Design Systematic review of the available literature. We searched Ovid MEDLINE, Ovid EMBASE, EBSCO SPORTDiscus and OpenGrey up to February 2013. Data extraction was carried out with a specially developed form. Data extracted related to the type of study (eg case control, cohort study etc.), methodological quality, sports included and oral health (including its impact on performance). Methodological quality was assessed using a modification of the Newcastle–Ottawa scale and CASP critical appraisal checklist. Setting Elite division. Participants Randomised controlled trials (RCTs) or observational studies with some measure of oral health used as an outcome. Studies assessing the impact of interventions on oral health were not included. Main outcome measurements Oral health, methodological quality of included studies. Results After screening 9208 citations, 38 studies remained. Most studies investigated trauma (n=31 papers, 82% of all included studies) with the remainder looking at oral health and other outcomes. Studies were subject to multiple methodological biases and use of a wide range of outcomes limited comparison between studies. In general head/face/dental trauma made up a very small proportion of injuries received by athletes during specific tournaments or over a fixed timespan with the exception of wrestling, ice hockey and football. Oral health as expressed by caries was poor in athletes. No studies to date had been designed to assess impact. Conclusions There is a lack of data on the oral health of elite athletes and its impact on performance.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".