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Análise de dentição mista: tomografia versus predição e medida radiográfica

2010· article· pt· W2067657926 on OpenAlexaff
Letícia Guilherme Felício, Antônio Carlos de Oliveira Ruellas, Ana Maria Bolognese, Eduardo Franzotti Sant’Anna, Mônica Tirre de Souza Araújo

Bibliographic record

VenueDental Press Journal of Orthodontics · 2010
Typearticle
Languagept
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsWestern University
Fundersnot available
KeywordsPhysicsHumanitiesNuclear medicineMedicineArt

Abstract

fetched live from OpenAlex

OBJETIVO: o objetivo dos autores desse estudo foi comparar o método de análise de dentição mista que utiliza tomografia computadorizada de feixe cônico para avaliar os diâmetros dos dentes intraósseos com os métodos de Moyers, Tanaka-Johnston e radiografias oblíquas em 45º. MÉTODOS: foram realizadas medidas, na arcada inferior, dos diâmetros mesiodistais dos incisivos permanentes irrompidos, nos modelos de gesso com auxílio de paquímetro digital e estimativa do tamanho de pré-molares e caninos permanentes ainda não irrompidos utilizando-se a tabela de Moyers e a fórmula de predição de Tanaka-Johnston. Nas radiografias oblíquas em 45º, caninos e pré-molares foram medidos utilizando-se o mesmo instrumento. Nas tomografias, as mesmas unidades dentárias foram aferidas por meio de ferramentas do programa Dolphin. RESULTADOS: a análise estatística revelou elevada concordância entre o método tomográfico e o radiográfico, e baixa concordância entre o tomográfico e os demais avaliados. CONCLUSÃO: a tomografia computadorizada de feixe cônico mostrou-se confiável para análise da dentição mista e apresenta algumas vantagens em relação aos métodos comparados: a observação e mensuração dos dentes intraósseos individualmente, com a possibilidade, contudo, de visualizá-los sob diferentes perspectivas e sem superposição de estruturas anatômicas.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.309
Teacher spread0.280 · 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
Published2010
Admission routes1
Has abstractyes

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