Effectiveness of intraoral splints in the treatment of migraine and tension‐type headache: A systematic review
Bibliographic record
Abstract
OBJECTIVES: The main objective of this systematic review was to assess the effectiveness of intraoral splints in treating migraine and tension-type headaches. MATERIAL AND METHODS: The article search was conducted within seven electronic databases (Medline, PubMed, Embase, CINAHL PLUS with full text, Cochrane Library Trials, Web of Science, and Scopus) with no date limits or language restrictions up to June 12, 2022. Strict inclusion and exclusion criteria were set for article selection. At the same time as data extraction, each study's risk of bias (RoB) was evaluated using the Cochrane tool to assess their RoB. Subsequently, the Cochrane Grading of Recommendations Assessment Development and Evaluation was used to evaluate the certainty of the evidence. RESULTS: Four controlled clinical trials were included. These trials were heterogeneous in terms of (1) diagnosis, (2) design of the intraoral splints, and (3) tools for reporting the results, which made it difficult to compile the data as well as evaluate its quality. Trials reported a reduction in the frequency of headache and pain intensity when using intraoral splints; however, this therapy was not superior to medications. CONCLUSIONS: The evidence is very low for the use of oral splints as a therapeutic alternative to medication in the treatment of migraine and/or tension-type headache.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".