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Record W3039375067 · doi:10.3390/jcm9072088

Pain and Oral-Health-Related Quality of Life in Orthodontic Patients During Initial Therapy with Conventional, Low-Friction, and Lingual Brackets and Aligners (Invisalign): A Prospective Clinical Study

2020· article· en· W3039375067 on OpenAlexaboutno aff
Laura Antonio-Zancajo, Javier Montero, Alberto Albaladejo, María Dolores Oteo-Calatayud, Alfonso Alvarado-Lorenzo

Bibliographic record

VenueJournal of Clinical Medicine · 2020
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)Oral healthProspective cohort studyDentistryMcGill Pain QuestionnaireSignificant differencePhysical therapyVisual analogue scaleSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

The aim of this study was to compare pain and its relationship with the oral quality of life of patients with different types of orthodontic appliances: conventional and conventional low-friction brackets, lingual brackets, and aligners. A prospective clinical study was carried out with a sample size of 120 patients (54 men, 66 women) divided into 4 groups of 30 patients each. The modified McGill questionnaire was used to measure pain at 4, 8, and 24 h and 2, 3, 4, 5, 6, and 7 days after the start of treatment, and the Oral Health Impact Profile-14 (OHIP-14) questionnaire was used to measure the oral-health-related quality of life (OHRQoL) in the first month of treatment. The maximum peak of pain was obtained between 24 and 48 h of treatment. It was found that patients in the lingual orthodontic group described lower levels of pain at all times analyzed, and their scores in the total OHIP-14 indicated less impact on their oral quality of life (1.3 ± 1.2, p < 0.01) compared with the other groups analyzed. There was little difference with the aligners group (Invisalign) (1.7 ± 1.9, p < 0.01). The technique used influences the pain and quality of life of patients at the start of orthodontic treatment.

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)0.012
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.139
GPT teacher head0.458
Teacher spread0.319 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations76
Published2020
Admission routes1
Has abstractyes

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