Tooth wear in young subjects: a discriminator between sleep bruxers and controls?
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: This study investigated whether the presence of tooth wear in young adults can help to discriminate patients with sleep bruxism (SB) from control subjects. MATERIALS AND METHODS: The tooth wear clinical scores and frequency of sleep masseter electromyographic activity of 130 subjects (26.6 +/- 0.5 years) were compared in this case-control study. Tooth wear scores (collected during clinical examination) for the incisors, canines, and molars were pooled or analyzed separately for statistics. Sleep bruxers (SBrs) were divided into two subgroups according to moderate to high (M-H-SBr; n = 59) and low (L-SBr; n = 48) frequency of masseter muscle contractions. Control subjects (n = 23) had no history of tooth grinding. The sensitivity and specificity of tooth wear versus SB diagnosis, as well as positive and negative predictive values (PPV and NPV), were calculated. One-way analysis of variance and the Mann-Whitey U test were used to compare groups. RESULTS: Both SBr subgroups showed significantly higher tooth wear scores than the control group for both pooled and separated scores (P < .001). No difference was observed between M-H-SBr and L-SBr frequency groups (P = .14). The pooled sum of tooth wear scores discriminates SBrs from controls (sensitivity = 94%, specificity = 87%). The tooth wear PPV for SB detection was modest (26% to 71%) but the NPV to exclude controls was high (94% to 99%). CONCLUSIONS: Although the presence of tooth wear discriminates SBrs with a current history of tooth grinding from nonbruxers in young adults, its diagnostic value is modest. Moreover, tooth wear does not help to discriminate the severity of SB. Caution is therefore mandatory for clinicians using tooth wear as an outcome for SB diagnosis.
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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 | 0.001 | 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 | 0.001 |
| 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 it