Intramuscular injection of nerve growth factor as a model of temporomandibular disorder: nature, time-course, and sex differences characterising the pain experience
Bibliographic record
Abstract
Background: Temporomandibular disorder (TMD) is a common condition that frequently transitions to chronic symptoms. Experimental pain models that mimic the symptoms of clinical TMD may be useful in understanding the mechanisms, and sex differences, present in this disorder. Here we aimed to comprehensively characterise the nature and time-course of pain, functional impairment and hyperalgesia induced by repeated intramuscular injection of nerve growth factor (NGF) into the masseter muscle, and to investigate sex differences in the NGF-induced pain experience. Methods: 94 healthy individuals participated in a longitudinal study with 30-day follow-up. NGF was injected into the right masseter muscle on Day 0 and Day 2. Participants attended laboratory sessions to assess pain (Numerical Rating Scale; NRS), functional limitation (mouth opening distance, Jaw Functional Limitation Scale; JFLS) and mechanical sensitization (pressure pain thresholds; PPTs) on Days 0, 2 and 5 and completed twice daily electronic pain dairies from Day 0 to day 30. Results: Peak pain averaged 2.0/10 (95 % CI: 1.6-2.4) at rest and 4.3/10 (95 % CI: 3.9-4.8) on chewing. Pain-free mouth opening distance reduced from 5.0 cm (95 % CI: 4.8-5.1 cm) on Day 0 to 3.7 cm (95 % CI: 3.5-3.9 cm) on Day 5. The greatest reduction in PPTs was observed over the masseter muscle. Females experienced higher pain, greater functional impairment, and greater sensitivity to mechanical stimuli than males. Conclusion: Intramuscular injection of NGF is a useful model with which to explore the mechanisms, and sex differences, present in clinical TMD.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".