Prevalence of temporomandibular disorders among psychoactive substances abusers: A systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has had a significant impact on substance abuse patterns in recent times. Many people have experienced increased stress, anxiety, and social isolation, which has led to higher rates of substance abuse and addiction. It impacts on the orofacial region, particularly temporomandibular joint (TMJ). This review was undertaken to assess the association between substance abuse and temporomandibular disorders. (TMDs). MATERIALS AND METHODS: The databases of PubMed, Google Scholar, Web of Science and Cochrane were searched for articles based of set PECO criteria. A comprehensive search using keywords of "Psychoactive substances", "Illegal substances", "substance abuse", "narcotics", "temporomandibular joint" and "temporomandibular joint disorders" yielded a total of 1405 articles. Modified Newcastle-Ottawa Scale for observational studies assessed the risk of bias of included studies. RESULTS: Two studies were reviewed. Samples recruited were either from rehabilitation centres or prisoners and fell in the second to fourth decade. A definite association was noted between psychoactive substance and TMDs. Moderate to low risk of bias was noted in all the studies evaluated. CONCLUSION: Further research is needed to better understand the nature of this relationship and the underlying mechanisms involved. It is important for healthcare providers to be aware of this potential association and to screen for substance abuse in patients with TMD symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".