Tongue strength as a clinical feature of oral health in neurological patients: A Systematic Review.
Bibliographic record
Abstract
Objective: Current oral health assessment has a compre-hensive view of the relationship between hard and soft tissues of the mouth as seen by orthodontics and prosthodontics in a healthy population. Despite knowing the influence this relationship has on functional outcomes such as swallowing and mastication, motor evaluation of soft tissue such as the tongue is still scarce. This lack of knowledge is even greater in individuals with a neurological condition. In this sense, the measurement of lingual strength has been addressed by some research as a key element accompanying oral rehabilitation in healthy populations. Acknowledging the importance of tongue strength in oral biomechanics, the Iowa Oral Performance Instrument (IOPI) has become a gold standard instrument. The purpose of this article was to search for scientific studies on tongue strength using the IOPI as a research tool in populations with neurological conditions, to know about its inclusion in the clinical practice and comprehensive oral health rehabilitation in this population. Material and Methods: A systematic search in five major databases was carried out based on the PRISMA Protocol. Searches were conducted in the PubMed, Medline, Lilacs, Web of Science and MedCarib databases including articles from 2007 to 2020. To generate the search in each database, three main constructs were developed: (1) "tongue strength IOPI"; (2) "Swallowing Disorders"; (3) "Neurological Diseases". Results: 152 studies were identified, 14 were included in the final review. The PEDro scale showed great heterogeneity in the level of evidence between the studies with only 5 RCTs and only two of them on lingual strength training. Conclusion: The IOPI was used mainly to measure tongue strength and only 36% as a clinical training device, which could contribute to improving oral health. The stroke was the most represented (79%).IIIISU.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| 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".