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Record W4411616038 · doi:10.1155/crid/3082753

The Use of the Surgical Guide for Placing Miniscrew in Treatment of Class II Subdivision: A Case Report With 2‐Year Follow‐Up

2025· article· en· W4411616038 on OpenAlexaff
Wenyong Liang

Bibliographic record

VenueCase Reports in Dentistry · 2025
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsOntario Medical Association
Fundersnot available
KeywordsMedicineOrthodonticsPremolarMolarDentistryOverjetDentition

Abstract

fetched live from OpenAlex

Background: Treatment of Class II subdivision can present a challenge for the clinician because of its asymmetry and possible midline deviation. This case report documents the use of a computer‐aided design/computer‐aided manufacturing (CAD/CAM) surgical guide for miniscrew placement in a Class II subdivision treatment. Case Presentation: A 17‐year and 1‐month‐old female presented with a skeletal Class I relationship, but mild mandibular skeletal and dental midline shift to the right relative to the facial midline. A full‐step Class II molar relationship on the right side and slight Class III molar relationship on the left side and a 4.0 mm deficiency of space in the maxillary were noticed. Using CAD/CAM technology, a surgical guide was designed virtually and 3D printed for predrilling. With the surgical guide, one ø1.3 mm twist drill was chosen to prepare a 4–5 mm deep hole in the alveolar process distobuccal to the maxillary right second premolar. A ø 1.4 m m × 8.0 m m miniscrew was inserted into the prepared hole. With this miniscrew, the unilateral Class II relationship was corrected successfully by distalization of the unilateral maxillary dentition on the Class II side after 13 months of treatment. Conclusion: Application of CAD/CAM surgical guide is very helpful for placement of the miniscrew. Class II subdivision may be treated by distalizing unilateral maxillary dentition on the Class II side using the miniscrew.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.050
GPT teacher head0.338
Teacher spread0.288 · 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 designCase report
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

Citations0
Published2025
Admission routes1
Has abstractyes

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