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Record W4406660999 · doi:10.1053/j.sodo.2025.01.001

Navigating oral medicine and pathology in orthodontic treatment

2025· review· en· W4406660999 on OpenAlexaff
Salima Asifali Sawani, Jonathan M. Chu, Hasti Mahdi Zadeh, Reid Friesen

Bibliographic record

VenueSeminars in Orthodontics · 2025
Typereview
Languageen
FieldDentistry
TopicOral and Maxillofacial Pathology
Canadian institutionsConcordia University of EdmontonUniversity of Alberta
Fundersnot available
KeywordsMedicineOral medicineOral and maxillofacial pathologyDentistry

Abstract

fetched live from OpenAlex

<h2>Abstract</h2> Orthodontic treatment intersects with oral medicine and pathology, emphasizing the need for a comprehensive and multidisciplinary approach to patient care. This review examines the identification, management, and orthodontic implications of conditions that may predate orthodontic care, arise during treatment, or have a history of recurrence. Key topics include ulcerative lesions such as herpes simplex virus (HSV) and recurrent aphthous stomatitis, benign soft tissue masses like fibromas, frictional keratosis, and traumatic ulcers, as well as intraosseous jaw lesions. The manuscript also explores systemic conditions and hypersensitivity reactions, including allergic contact stomatitis and lichenoid responses to dental materials, along with common reactive gingival pathologies like pyogenic granuloma and peripheral ossifying fibroma. By prioritizing conditions that are frequently encountered in orthodontic settings, this review provides evidence-based guidance to optimize treatment planning, minimize disruptions, and enhance patient outcomes. Additionally, this review highlights the importance of early detection, tailored management strategies, and collaboration with appropriate medical and dental specialists. By integrating oral medicine principles into orthodontic care, orthodontists can address both pre-existing and treatment-related pathologies, ensuring a holistic approach that supports both orthodontic success and overall oral health.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.079
GPT teacher head0.421
Teacher spread0.342 · 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 designOther design
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

Citations3
Published2025
Admission routes1
Has abstractyes

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