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Record W3037721174 · doi:10.3390/app10124276

Comparison of Pain Perception between Clear Aligners and Fixed Appliances: A Systematic Review and Meta-Analysis

2020· review· en· W3037721174 on OpenAlexaboutno aff
Dinis Pereira, Vanessa Machado, João Botelho, Luís Proença, José João Mendes, Ana Sintra Delgado

Bibliographic record

VenueApplied Sciences · 2020
Typereview
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scaleMeta-analysisRandomized controlled trialMEDLINEPain perceptionPhysical therapyProtocol (science)Alternative medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.168
GPT teacher head0.405
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations35
Published2020
Admission routes1
Has abstractyes

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