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Record W4396237846 · doi:10.2147/jmdh.s433693

Tooth-Bone-Borne vs Bone-Borne Rapid Maxillary Expanders on Dentoskeletal Changes

2024· article· en· W4396237846 on OpenAlexaff
Javier Echarri-Nicolás, María José González‐Olmo, Pablo Echarri-Lobiondo, Manuel O. Lagravère, Martín Romero

Bibliographic record

VenueJournal of Multidisciplinary Healthcare · 2024
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDentistryMedicineOrthodonticsComputer science

Abstract

fetched live from OpenAlex

Purpose: This study compared the insertion and thickness of the cortical bone, the parallelism of the midpalatal suture, and the skeletal transverse dimension using a microimplant-assisted rapid palatal expansion (MARPE) with those produced by bone-anchored maxillary expander (BAME) in adults. Patients and Methods: The sample comprised 18 patients (mean age, 29.9±9.4 years) selected for the MARPE group and 18 (mean age 24.8±6.8 years) selected for the BAME group. Cone-Beam computed tomography scans taken just before and after the expansion. Results: After treatment, there was a significant palatal depth decrease. In addition, maxillary and mandibular width were increased in both groups. Regarding the cortical bone insertion and cortical bone width, it is shown an increase in both groups but those changes are not significant. However, the anterior suture opening effect occurs more frequently in the BAME than in the MARPE device. There are no differences in the frequency of opening of the midpalatal suture in the cases treated with MARPE and BAME, in both cases the most frequent opening morphology is parallel, independently of midpalatal suture maturation. Conclusion: There are no differences in the use of MARPE or BAME in the parallelism of the middle palatal suture, the cortical bone insertion, and thickness according to the maturation stages of the midpalatal suture but significantly increases the transverse dimensions of the maxillary skeleton.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.040
GPT teacher head0.350
Teacher spread0.310 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

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