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Record W4389169879 · doi:10.18066/inic0558.23

A VISCOSSUPLEMENTAÇÃO COMO TRATAMENTO ALTERNATIVO DE DISFUNÇÕES DAS ARTICULAÇÕES TEMPOROMANDIBULARES (ATM)

2023· article· pt· W4389169879 on OpenAlexaff
Heloisa de Andrade Freitas, Larissa Mansilha, Marília Gabriela de Oliveira Lopes, Milagros Del Valle El Abras Ankha

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsPhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

As Disfunções Temporomandibulares (DTM) são alterações estruturais e funcionais da Articulação Temporomandibular (ATM) que tem como sinais e sintomas: ruídos, perda de função e dores por conta do deslocamento discal.Casos de DTM estão cada vez mais frequentes; afetando a qualidade de vida do indivíduo.Cabe ao cirurgião-dentista estar preparado para diagnosticar e recorrer a diferentes formas de tratamentos dessa patologia.Uma técnica alternativa é a da viscossuplementação com ácido hialurônico (AH), que funciona como um preenchimento redutor de atrito.Um procedimento simples, minimamente invasivo, seguro e de ótimo custo benéfico.Neste trabalho foram selecionados artigos científicos de bases indexadas como: SCIELO, BIREME, PUBMED e Google Acadêmico, do período de 2008 a 2021, para uma revisão de literatura da efetividade da viscossuplementação da ATM com ácido hialurônico no tratamento das disfunções temporomandibulares.Após avaliado métodos de tratamento, aspectos clínicos, diagnóstico e a efetividade da técnica, concluiu-se que a viscossuplementação com

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.078
GPT teacher head0.462
Teacher spread0.384 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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