Comparison of speech effects among labial, lingual, and aligner appliances: a systematic review
Bibliographic record
Abstract
Abstract Background: Speech sound disorders are produced irrespective of the type of orthodontic appliance. The present systematic review aimed to compare the speech impediments created during orthodontic treatment with the labial appliances (LA), lingual appliances (LI), and orthodontic aligners (OA). Methods: The studies were searched from PubMed, Scopus, Web of Science, Embase, and Cochrane Library from 2000 to 25 Feb 2022. A manual search was also performed. The study’s quality was assessed by the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Quality Assessment Form. Two reviewers performed study selection and data extraction. Results: From a total of 1298 articles, 21 studies were selected, including 3 randomized clinical trials (RCTs), 15 prospective studies, 2 retrospective studies, and 1 cross-sectional study. Two RCTs were assessed as having a "low" risk of bias, and one was considered unclear. Twelve nonrandomized studies were classified as "high" and six as "moderate" quality. Six studies compared LA and LI, three studies compared LA and OA, one study compared LI and OA, and eleven studies only evaluated single orthodontic appliances. Conclusion: Based on the evidence available, LA seemed to impact minimally on speech, and the duration was the shortest; LI caused more speech impediments and had greater difficulty adapting; OA produced mild to moderate speech impediments, and the adaptation time was between the two.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".