Preclinical perspectives on disorders of the temporomandibular joint: Tracing the past, navigating the present, and shaping the future
Bibliographic record
Abstract
Temporomandibular disorders (TMDs) are complex conditions characterized by orofacial pain and dysfunction, affecting a significant portion of the population. TMDs may involve joint and/or muscle pain, dysfunction (e.g., noise, limited or altered jaw movements), or both, leading to a marked decrease in quality of life. Patients often experience functional limitations that hinder eating, speaking, and daily activities. Additionally, TMDs are frequently associated with psychological distress, including anxiety and depression, which further impacts overall well-being. Despite the profound individual and societal impact of TMDs, effective therapies remain elusive, partly due to deficiencies in translational research. A primary limitation in the TMD field is the scarcity of animal models that accurately replicate disease features in humans. This may ultimately be due to species differences, but likely also reflects the etiological and symptomatic heterogeneities of TMDs, as there are over 30 different conditions in this umbrella term. Both factors pose a significant challenge in developing and using animal models for TMD research. This review highlights preclinical TMD research to enhance clinical care, focusing on anatomy/physiology, pain and behavior models, functional and tissue modeling, biopsychosocial factors, and technological considerations. The "TMD Research Community" collaborated to produce this review, with the Discussion offering a proposal for a path forward.
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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".