Effectiveness of T-Scan Technology in Identifying Occlusal Interferences and its Role in the Management of Temporomandibular Disorders: A Systematic Review
Bibliographic record
Abstract
Introduction: Occlusion is a significant issue that affects the masticatory system’s health. Temporomandibular Disorders (TMD) have long been linked to occlusal interferences. Identification and management of such disorders using a T-scanguided approach have been gaining popularity; however, the effectiveness is still not established. Aim: To evaluate the effect of T-scan on the reduction of Visual Analogue Scale (VAS) scores of pain and improvement in the symptoms associated with TMD. The benefits of reducing Disocclusion Time (DT) were also evaluated. Materials and Methods: The Medical Literature Analysis and Retrieval System Online (MEDLINE) database via PubMed and Excerpta Medica Database (EMBASE) were searched for studies reporting the use of T-scan in Temporomandibular Joint (TMJ) disorders to check the DT and patient-related outcomes. The search was performed from January 1991 to November 2022. A total of 10 studies were included in the systematic review, which includes clinical studies, observational studies, and interventional studies. Data extraction was performed, and risk of bias assessment was done using the Newcastle-Ottawa scale (NOS) for non randomised studies, and the Cochrane tool was utilised for randomised clinical trials. A qualitative analysis of all the studies was carried out. Results: The T-scan-guided occlusal correction or equilibration procedures led to improvement in subjective symptoms and VAS in TMD patients. Reduction in DT positively affected muscle activity, causing relief of chronic symptoms. Conclusion: As per the findings of the present review, T-scan technology can be successfully used in the precise identification and diagnosis of occlusal discrepancies in patients suffering from myofascial symptoms.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".