Pain level between clear aligners and fixed appliances: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To assess if there is any difference in pain levels between orthodontic treatment with clear aligners or fixed appliances. MATERIALS AND METHODS: An electronic search was completed in PubMed, The Cochrane Database, Web of Science, Scopus, Lilacs, Google Scholar, Clinical Trials, and OpenGrey databases without any restrictions until February 2019. All comparative study types contrasting pain levels between clear aligners and fixed appliances were included. The risk of bias (RoB) was assessed using the Newcastle-Ottawa Scale, ROBINS-I-Tool, or ROB 2.0 according to the study design. The level of evidence was assessed through the GRADE tool. RESULTS: After removal of duplicates, exclusion by title and abstract, and reading the full text, only seven articles were included. Five were prospective non-randomized clinical trials (CCT), one was a cross-sectional study, and one was a randomized clinical trial (RCT). Two studies presented a high RoB, three a moderate RoB, and two a low RoB (including the RCT). A meta-analysis was not performed because of clinical, statistical, and methodological heterogeneity. Most of the studies found that pain levels in patients treated with Invisalign were lower than those treated with conventional fixed appliances during the first days of treatment. Differences disappeared thereafter. No evidence was identified for other brands of clear aligners. CONCLUSIONS: Based on a moderate level of certainty, orthodontic patients treated with Invisalign appear to feel lower levels of pain than those treated with fixed appliances during the first few days of treatment. Thereafter (up to 3 months), differences were not noted. Malocclusion complexity level among included studies was mild. Pain is one of many considerations and predictability and technical outcome are more important, mainly considering that the difference does not seem to occur after the first months of the orthodontic treatment.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".