Effects of various oral exercises on swallowing function in the elderly: A systematic review
Bibliographic record
Abstract
Oral exercises as a rehabilitation strategy can be performed by the elderly at any time and place without any complications or financial burden. Therefore, an oral exercise that is easy to perform and follow in daily life, can effectively improve the oral health of the elderly. This systematic review was conducted to investigate the effect of oral exercises on swallowing function in the elderly. A comprehensive systematic search was conducted on international electronic databases such as Scopus, PubMed, Web of Science, as well as on Iranian electronic databases such as Iranmedex, and Scientific Information Database from the earliest to April 1, 2023. The Joanna Briggs Institute (JBI) critical appraisal checklist was used to assess the quality of randomized control trials and quasi-experimental studies. A total of 728 elderly people participated in fourteen studies. Based on the results of the present systematic review, ten interventions including “lingual resistance exercise with Iowa oral performance instrument”, “exercise for swallowing”, “oral exercise”, “expiratory muscle strength training”, “swallow resistance exercise”, “swallowing exercises”, “oral neuromuscular training”, “simple oral exercise (SOE)”, “tongue strengthening exercise”, “SOE and chewing gum exercise with SOE” improved swallowing function in the elderly. However, the other four interventions including “swallowing exercises”, “resistance training”, “effortful swallowing exercise”, and “oral exercises” did not affect improving swallowing function in the elderly. Health managers and policymakers can improve the swallowing performance of the elderly by implementing training protocols related to oral exercise in addition to routine interventions.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.003 | 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".