Bibliographic record
Abstract
The oro-facial sensorimotor system is a unique system significantly distinguished from the spinal sensorimotor system. The jaw muscles are involved in mastication, swallowing and articulatory speech movements and their integration with respiration. These sensorimotor functions are vital for sustaining life and necessitate complex neuromuscular processing to provide for exquisite sensorimotor control of numerous oro-facial muscles. The function of the jaw muscles in relation to sensorimotor control of these movements may be subject to ageing-related declines. This review will focus on peripheral, brainstem and higher brain centre mechanisms involved in reflex regulation and sensorimotor coordination and control of jaw muscles in healthy adults. It will outline the limited literature bearing on age-related declines in jaw sensorimotor functions and control including reduced biting forces and increased risk of impaired chewing, speaking and swallowing. The mechanisms underlying these alterations include age-related degenerative changes within the peripheral neuromuscular system and in brain regions involved in the generation and control of jaw movements. In the light of the vital role of jaw sensorimotor functions in sustaining life, normal ageing involves compensatory mechanisms that utilise the neuroplastic capacity of the brain and the recruitment of additional brain regions involved in sensorimotor performance and closely associated functions (e.g. cognition and memory). However, these regions are themselves susceptible to detrimental age-related changes. Thus, better understanding of the peripheral and central mechanisms underlying age-related sensorimotor impairment is crucial for developing improved treatment approaches to prevent or cure impaired jaw sensorimotor functions and to thereby improve health and quality of life.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".