Comparison of Pain Perception between Clear Aligners and Fixed Appliances: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
We aimed to compare the pain discomfort levels between clear aligners and fixed appliances at multiple time points. Four electronic databases (Pubmed, Medline, CENTRAL and Scholar) were searched up to May 2020. There were no year or language restrictions. Randomized clinical trials and case–control studies comparing pain perception through pain visual analog scale (VAS) in patients treated with clear aligners and with fixed appliances were included. Risk of bias within and across studies was assessed using Cochrane tool and Newcastle–Ottawa Scale (NOS) approach. Random-effects meta-analysis were conducted. VAS score and analgesic consumption were collected. Random-effects meta-analyses were used to synthesize available data. Following the review protocol, five articles met the inclusion criteria and were included, with a total of 273 participants (177 females, 96 males). Overall, clear aligners were associated with significantly less pain than fixed appliances during the first seven days of orthodontic treatment. Patients treated with clear aligners experience less pain discomfort than those treated with fixed appliances and consume less analgesics, with SORT A recommendation.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.008 | 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 | 0.002 |
| 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".