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Record W3009018008 · doi:10.2319/052719-362.1

Esthetic perception of facial profile changes in Class II patients treated with Herbst or Forsus appliances

2020· article· en· W3009018008 on OpenAlexaff
Alexa Helena Köhler Moresca, Nathaly Dias de Moraes, Francielle Topolski, Carlos Flores‐Mir, Alexandre Moro, Ricardo Moresca, Gisele Maria Correr

Bibliographic record

VenueThe Angle Orthodontist · 2020
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineSignificant differenceOrthodonticsWilcoxon signed-rank testDentistryMean differenceMann–Whitney U testConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the esthetic perceptions of orthodontists and laypersons for facial profile changes after orthodontic treatment using Herbst or Forsus appliances. MATERIALS AND METHODS: Pre- and posttreatment facial profile contour images of 20 Class II patients treated with Herbst (group H; n = 10) and Forsus (group F; n = 10) appliances were analyzed by 30 orthodontists and 30 laypersons, who graded them from 1 (unattractive) to 10 (very attractive) using a visual analog scale. Two assessments were carried out with a 15 day-interval. In the first evaluation, 40 images were presented in a random sequence. In the second evaluation, initial and final facial profile images of each patient were randomly presented side by side. To compare groups in relation to treatment method, Mann-Whitney tests were used. To evaluate differences between time points, Wilcoxon tests were used. RESULTS: In the first evaluation, there was a significant difference between initial and final images only for group H, for both laypersons (P = .017) and orthodontists (P = .037). There was also a significant difference between laypersons and orthodontists in their ratings of posttreatment Herbst appliance profiles (P = .028). There was no significant difference between initial and final facial profile images for group F and no significant differences between or within evaluator groups in their ratings of initial or final Forsus appliance profiles. In the second evaluation, there was a significant difference between appliance groups only for laypersons, who considered cases treated with the Herbst appliance more attractive than those treated with the Forsus (P = .031). Laypersons also considered Herbst profiles more attractive than did orthodontists (P = .047). CONCLUSIONS: Class II malocclusion treatment using the Herbst appliance may produce a more esthetically improved facial profile silhouette compared with Forsus appliances. The magnitude of perceived changes may not be considered clinically relevant.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.272
Teacher spread0.237 · 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 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

Citations16
Published2020
Admission routes1
Has abstractyes

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