MétaCan
Menu
Back to cohort
Record W4399867963 · doi:10.4041/kjod23.224

A scoping review of cephalometric normative data in children

2024· review· en· W4399867963 on OpenAlexafffund
Thi Ngoc Lan Nguyen, Akanksha Cambala, Manuela Hrit, Elizabeth A. Zimmermann

Bibliographic record

VenueThe Korean Journal of Orthodontics · 2024
Typereview
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversité de MontréalMcGill University Health Centre
FundersMcGill University
KeywordsNormativeOrthodonticsPsychologyDevelopmental psychologyMedicineEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Objective: Understanding the orofacial characteristics and growth patterns in children is essential for both orthodontics and research on children with orofacial abnormalities. However, a concise resource of normative data on the size and relative position of these structures in different populations is not available. Our objective was to aggregate normative data to assess the growth of the orofacial skeletal structures in children with a well-balanced face and normal occlusion. Methods: The MEDLINE, Embase, and Scopus databases were searched. Inclusion criteria included longitudinal and cross-sectional studies on cephalometric measurement of skeletal tissues and a study population ≤ 18 years with a well-balanced face and normal occlusion. Key study parameters were extracted, and knowledge was synthesized. A quality appraisal was performed using a 10-point scale. Results: The final selection comprised of 12 longitudinal and 33 cross-sectional studies, the quality of which ranged from good to excellent. Our results showed that from childhood to adulthood, the length of the cranial base increased significantly while the cranial base angle remained constant; both the maxilla and mandible moved forward and downward. The profile becomes straighter with age. Conclusions: Growth patterns in children with a well-balanced face and normal occlusion follow accepted theories of growth.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
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 head0.126
GPT teacher head0.430
Teacher spread0.304 · 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 designSystematic review
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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Korean Journal of OrthodonticsSame topicOrthodontics and Dentofacial OrthopedicsFrench-language works237,207