Impact of temporomandibular disorders on oral health‐related quality of life: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The management of temporomandibular disorders (TMDs) requires a comprehensive approach that considers multiple factors, including the impact of oral health-related quality of life (OHRQoL). Through this investigation we aim to assess the impact of OHRQoL played in a TMD-afflicted individual. METHODS: Using keywords relevant to our research, such as "Oral health related quality of life," "Oral hygiene," "Temporomandibular joint" and "Temporomandibular disorders," a comprehensive search across multiple online databases was carried out, yielding a total of 632 studies at the preliminary stage of the review. Modified New Castle Ottawa scale was used to assess the quality of studies included. RESULTS: Eight studies were included in the review, out of which six were eligible for further meta-analysis. The studies included in this review employed various OHRQoL measures, including the Oral Health Impact Profile-14 (OHIP-14), the Short-Form 36 Health Survey (SF-36) and the OHIP- 49. All the studies demonstrated significant effect of TMDs on the OHRQoL of the target population under study. CONCLUSION: The impact of OHRQoL on the management of TMD was deemed to be significant. The comprehensive management of TMD should consider the impact of the condition on the individual's daily life and incorporate interventions that address both the physical and psychological aspects of the condition. By improving OqL, individuals with TMD can experience improved overall well-being and quality of life.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".