Oral dyskinesia: a clinical overview.
Bibliographic record
Abstract
PURPOSE: Dentists may be the first health care professionals to recognize unusual and abnormal oral movements collectively termed oral dyskinesias. The aims of this clinical overview are to raise the dental community's awareness about this important and complex topic and describe the clinical features and management of the main entities. MATERIALS AND METHODS: A MEDLINE search of the different entities reported in the English and French literature was conducted. The main findings of a field study on oral dyskinesia were also reviewed. RESULTS: Involuntary movement disorders are often drug related. In other cases, excessive oral movements may occur at any age in relation to various neuropsychiatric conditions. Orofacial dystonia apparently triggered by dental procedures has also been reported. Edentulousness has been associated with oral stereotypes. In a survey of 352 edentulous elderly individuals attending daycare centers, only 7% displayed visible oral sterotypes, and ill-fitting dentures were suggested as a possible triggering factor for the majority. CONCLUSION: A multidisciplinary evaluation is desirable in the care of individuals with oral dyskinesia and in the selection of those who may benefit from a prosthodontic approach. A good knowledge of potentially offending drugs may allow avoidance of unnecessary procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".