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Record W2343542873 · doi:10.1111/scd.12175

Salivary parameters and oral health in the Moebius syndrome

2016· article· en· W2343542873 on OpenAlexafffund
Maria Carolina Martins Mussi, Eduardo Buozi Moffa, Talita Castro, Adriana Ortega, Gabrielle Coelho Freitas, Mariana Minatel Braga, Walter L. Siqueira, Marina Gallottini

Bibliographic record

VenueSpecial Care in Dentistry · 2016
Typearticle
Languageen
FieldMedicine
TopicPectus Deformity Diagnosis and Treatment
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCanada Foundation for Innovation
KeywordsMedicineOral healthDermatologyDentistry

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate salivary characteristics, such as flow rate, buffering capacity and amylase activity, and associate these parameters with oral health in individuals with Moebius syndrome (MS) and controls. METHODS: The researchers assessed caries (ICDAS), periodontal disease (PSR), and plaque (Silness Löe) indexes in both groups. Unstimulated, stimulated, and bilateral parotid saliva were also collected. The salivary flow (milliliters/minute) and α-amylase activity were measured in all samples. The α-amylase activity was determined by maltose production. The buffering capacity was measured in stimulated saliva by titration of 0.01N HCl. RESULTS: The occurrence of carious lesions, related to cut-off 2, as well as the occurrence of periodontal disease, was significantly higher (p > 0.05) in participants with MS when compared to the control group. Also, MS individuals presented decreased salivary flow, decreased buffering capacity, and amylase activity. CONCLUSIONS: MS individuals present salivary alterations, which can be related to higher risk for caries.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.317
Teacher spread0.290 · 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 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

Citations22
Published2016
Admission routes2
Has abstractyes

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