Tooth wear in young subjects: a discriminator between sleep bruxers and controls?
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".