Assessment of pain sensitivity in patients with deep bite and sex- and age-matched controls.
Bibliographic record
Abstract
AIMS: To compare pain sensitivity between deep bite patients and a sex- and age-matched control group with normal occlusion. METHODS: Pain sensitivity was assessed by injections of the excitatory amino acid glutamate into the masseter and brachioradialis muscles. Intensity of glutamate-evoked pain was scored by the subjects ( n = 60) on a 0 to 10 cm visual analog scale. Subjects drew the perceived pain area on a face and arm chart and described the quality of pain on the McGill Pain Questionnaire. Thresholds for cold detection, cold pain, cold tolerance, warmth detection, heat pain, and heat tolerance were assessed on the masseter and brachioradialis muscles. Pressure pain threshold and pain tolerance threshold were determined on the temporomandibular joint, masseter, anterior temporalis, and brachioradialis muscles. The differences between groups, age, and gender were tested by two-way ANOVA, and the significant differences were then tested for the effect of the presence of temporomandibular disorder (TMD) by linear regression. RESULTS: Glutamate-evoked pain intensity was significantly different between groups with no gender differences. Quality of pain did not vary between groups, but significant gender-related differences were observed. Significant differences in thermal sensitivity between groups and gender were found, whereas mechanical sensitivity did not vary between groups but between genders. None of the significant differences were due to the effect of TMD. CONCLUSION: These data provide further evidence of gender-related differences in somatosensory sensitivity and for the first time indicate that subjects with deep bite may be more sensitive to glutamate-evoked pain and thermal stimuli.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".