Diagnostic group differences in temporomandibular joint energy densities
Bibliographic record
Abstract
OBJECTIVES: Cartilage fatigue, due to mechanical work, may account for precocious development of degenerative joint disease in the temporomandibular joint (TMJ). This study compared energy densities (mJ/mm³) in TMJs of three diagnostic groups. SETTING AND SAMPLE POPULATION: Sixty-eight subjects (44 women, 24 men) gave informed consent. Diagnostic criteria for temporomandibular disorders (DC/TMD) and imaging were used to group subjects according to presence of jaw muscle or joint pain (+P) and bilateral disk displacement (+DD). MATERIAL AND METHODS: Subjects (+P+DD, n=16; -P+DD, n=16; and -P-DD, n=36) provided cone-beam computed tomography and magnetic resonance images, and jaw-tracking data. Numerical modeling was used to determine TMJ loads (Fnormal). Dynamic stereometry was used to characterize individual-specific data of stress-field dynamics during 10 symmetrical jaw-closing cycles. These data were used to estimate tractional forces (Ftraction). Energy densities were then calculated as W/Q (W=work done or mechanical energy input=tractional force×distance of stress-field translation, Q=volume of cartilage). anova and Tukey-Kramer post hoc analyses tested for intergroup differences. RESULTS: Mean±standard error energy density for the +P+DD group was 12.7±1.5 mJ/mm³ and significantly greater (all adjusted p<0.04) when compared to -P+DD (7.4±1.4 mJ/mm³) and -P-DD (5.8±0.9 mJ/mm³) groups. Energy densities in -P+DD and -P-DD groups were not significantly different. CONCLUSION: Diagnostic group differences in energy densities suggest that mechanical work may be a unique mechanism, which contributes to cartilage fatigue in subjects with pain and disk displacement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".