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Record W2898512366 · doi:10.1590/2317-1782/20182017216

O gênero e a idade influenciam as dimensões do palato duro? Revisão sistemática da literatura

2018· review· pt· W2898512366 on OpenAlexaboutno aff
Luana Cristina Berwig, Mariana Marquezan, Jovana de Moura Milanesi, Márlon Munhoz Montenegro, Thiago Machado Ardenghi, Ana Maria Toniolo da Silva

Bibliographic record

VenueCoDAS · 2018
Typereview
Languagept
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsnot available
Fundersnot available
KeywordsCochrane LibrarySagittal planeMedicineObservational studyHard palateOrthodonticsWeb of scienceHumanitiesDentistryMeta-analysisArt

Abstract

fetched live from OpenAlex

PURPOSE: Analyze the influence of gender and age on hard palate dimensions and verify the reference parameters available in the literature. RESEARCH STRATEGIES: Two reviewers independently performed a search at the Cochrane Library, PubMed-Medline and Web of Knowledge databases using descriptors according to the syntax rules of each database. SELECTION CRITERIA: Observational or experimental human studies evaluating the dimensions of the hard palate or maxillary dental arch, with at least one transverse, vertical or sagittal plane measurement, in normal occlusions or class I malocclusions, and comparisons of the dimensions between genders and/or ages. DATA ANALYSIS: Descriptive analysis with the following subdivisions: design, sample, evaluation instruments, measurements in millimeters, and statistical analysis. Quality of the included studies was verified by the Newcastle - Ottawa Quality scale. RESULTS: Eighteen studies were selected and 11 presented results for hard palate or maxillary dental arch dimensions according to gender, six in age and gender and one in age only. CONCLUSION: The dimensions were larger in males and progressive increase in the measurements was observed from birth to the permanent dentition period.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0060.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.029

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 head0.052
GPT teacher head0.364
Teacher spread0.312 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations11
Published2018
Admission routes1
Has abstractyes

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