Brief Pain Inventory and McGill Pain Questionnaire in Assessing the Patients with Temporomandibular Joint Disorders – A Cross-Sectional Study
Bibliographic record
Abstract
Background: The temporomandibular joint, where the mandible articulates with the skull, is renowned for its complexity. Temporomandibular disorders (TMDs) are the second most prevalent musculoskeletal affliction, causing pain and disability, underscoring their impact on well-being. Objective: To assess the efficacy of the Brief Pain Inventory and McGill Pain Questionnaire in TMD pain evaluation. Material and Methods: This study included 100 patients diagnosed with TMD who were asked to complete two questionnaires: the Brief Pain Inventory and the McGill Pain Questionnaire; the responses provided by the patients were collected and subjected to statistical analysis. Results: Among a cohort of 100 patients, the mean values for the duration and intensity of pain associated with TMD using Brief Pain Inventory manifested statistical significance, underscored by a P value of 0.001. The mean values of the Pain Rating Index and Present Pain Intensity, as determined through the McGill Pain Questionnaire across the study population, exhibited statistical significance, registering a P value of 0.001. Conclusion: The Brief Pain Inventory is most useful when compared with the McGill pain questionnaire in assessing the pain in patients with TMD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".