Biological and psychological factors affecting the sensory and jaw motor responses to orthodontic tooth movement
Bibliographic record
Abstract
Orthodontic tooth movement (OTM) is associated with an inflammatory response, tooth pain (i.e. orthodontic pain) and changes in dental occlusion. Clinical realms and research evidence suggest that the sensory and jaw motor responses to OTM vary significantly among individuals. While some adjust well to orthodontic procedures, others may not and can experience significant pain or not adjust to occlusal changes. This is of concern, as clinicians cannot anticipate an individual's sensorimotor response to OTM. Converging evidence shows that some psychological states and traits significantly affect the sensorimotor response to OTM and may considerably affect an individual's adaptation to orthodontic or other dental procedures. We performed a topical review to synthesize the available knowledge about the behavioural mechanisms regulating the sensorimotor response to OTM, with the intent of informing orthodontic practitioners and researchers about specific psychological states and traits that should be considered while planning orthodontic treatment. We report on studies focusing on the role of anxiety, pain catastrophising, and somatosensory amplification (i.e. bodily hypervigilance), on sensory and jaw motor responses. Psychological states and traits can significantly affect sensory and jaw motor responses and a patient's adaptation to orthodontic procedures, although large interindividual variability exists. Clinicians can use validated instruments (checklists or questionnaires) to collect information about patients' psychological traits, which can assist in identifying those individuals who may not adjust well to orthodontic procedures. The information included in this manuscript also assists researchers investigating the effect of orthodontic procedures and or/appliances on orthodontic pain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".