Can you be too old for oral implants? An update on ageing and plasticity in the oro‐facial sensorimotor system
Bibliographic record
Abstract
This review focuses on the capacity of the brain for plasticity and the utility and efficacy of oral implants in helping to restore oro-facial sensorimotor functions, especially in elderly patients. The review first outlines the components of the oro-facial sensorimotor system which encompasses both oro-facial tissues and a number of brain regions. One such region is the sensorimotor cortex that controls the activity of the numerous oro-facial skeletal muscles. These muscles are involved in a number of functions including reflexes and the more complex sensorimotor functions of mastication, swallowing and speech. The review outlines the use by the brain of sensory inputs from oro-facial receptors in order to provide for exquisite sensorimotor control of the activity of the oro-facial muscles. It highlights the role in this sensorimotor control played by periodontal mechanoreceptors and their sensory inputs to the brain, and how oral implants in concert with the plastic capacity of the brain may, at least in part, compensate for reduced sensorimotor functioning when teeth are lost. It outlines findings of ageing-related decrements in oro-facial sensorimotor functions and control. The changes in oro-facial tissues and the brain that underlie these ageing-related functional alterations are also considered, along with adaptive and compensatory processes that utilise the brain's capacity for plasticity. The review also notes the evidence that rehabilitation that incorporates adjunctive approaches such as sensorimotor training paradigms in addition to oral prostheses such as implants may enhance these processes and help maintain or facilitate recovery of sensorimotor functioning in the elderly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".