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Record W2904190865 · doi:10.1016/j.bjorl.2018.10.012

Using craniofacial characteristics to predict optimum airway pressure in obstructive sleep apnea treatment

2018· article· en· W2904190865 on OpenAlexaff
Thays Crosara Abrahão Cunha, Thaís Moura Guimarães, Fernanda R. Almeida, Fernanda Louise Martinho Haddad, Luciana Godoy, Thúlio Marquez Cunha, Luciana Oliveira e Silva, Sérgio Tufik, Lia Bittencourt

Bibliographic record

VenueBrazilian Journal of Otorhinolaryngology · 2018
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British Columbia
FundersAssociação Fundo de Incentivo à PesquisaFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São PauloUniversidade Federal de São Paulo
KeywordsMedicinePolysomnographyObstructive sleep apneaAirwayContinuous positive airway pressureApneaCraniofacialAnesthesiaBody mass indexSleep apneaGold standard (test)AnthropometryHypopneaOrthodonticsInternal medicine

Abstract

fetched live from OpenAlex

Manual titration is the gold standard to determinate optimal continuous positive airway pressure, and the prediction of the optimal pressure is important to avoid delays in prescribing a continuous positive airway pressure treatment. To verify whether anthropometric, polysomnographic, cephalometric, and upper airway clinical assessments can predict the optimal continuous positive airway pressure setting for obstructive sleep apnea patients. Fifty men between 25 and 65 years, with body mass indexes of less than or equal to 35 kg/m2 were selected. All patients had baseline polysomnography followed by cephalometric and otolaryngological clinical assessments. On a second night, titration polysomnography was carried out to establish the optimal pressure. The average age of the patients was 43 ± 12.3 years, with a mean body mass index of 27.1 ± 3.4 kg/m2 and an apnea–hypopnea index of 17.8 ± 10.5 events per hour. Smaller mandibular length (p = 0.03), smaller atlas–jaw distance (p = 0.03), and the presence of a Mallampati III and IV (p = 0.02) were predictors for higher continuous positive airway pressure. The formula for the optimal continuous positive airway pressure was: 17.244 − (0.133 × jaw length) + (0.969 × Mallampati III and IV classification) − (0.926 × atlas–jaw distance). In a sample of male patients with mild-to-moderate obstructive sleep apnea, the optimal continuous positive airway pressure was predicted using the mandibular length, atlas–jaw distance and Mallampati classification. A titulação manual é o padrão-ouro para determinar a pressão ideal para o tratamento com a pressão positiva contínua nas vias aéreas; e a predição da pressão ideal é importante para evitar retardos na sua prescrição. Verificar se as avaliações clínicas antropométricas, polissonográficas, cefalométricas e das vias aéreas superiores podem predizer a configuração ideal da pressão do aparelho de pressão positiva contínua nas vias aéreas para pacientes com apneia obstrutiva do sono. Foram selecionados 50 homens entre 25 e 65 anos, com índice de massa corporal menor ou igual a 35 kg/m2. Todos os pacientes fizeram polissonografia basal, seguida de avaliações clínicas cefalométricas e otorrinolaringológicas. Na segunda noite, foi feita polissonografia de titulação para estabelecer a pressão ideal. A média de idade dos pacientes foi de 43 ± 12,3 anos, com índice de massa corporal médio de 27,1 ± 3,4 kg/m2 e índice de apneia-hipopneia de 17,8 ± 10,5 eventos por hora. Menor comprimento mandibular (p = 0,03), menor distância atlas-maxila (p = 0,03) e a presença de Mallampati III e IV (p = 0,02) foram preditores de pressão mais elevada. A fórmula para a pressão positiva contínua nas vias aéreas foi: 17,24 − (0,133 × comprimento da mandíbula) + (0,969 × classificação de Mallampati III e IV) − (0,926 × distância atlas-mandíbula). Em uma amostra de homens com apneia obstrutiva do sono leve a moderada, a pressão positiva contínua nas vias aéreas foi predita com o comprimento mandibular, a distância atlas-mandíbula e a classificação de Mallampati.

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)0.003
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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0020.000

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 head0.026
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".

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Citations7
Published2018
Admission routes1
Has abstractyes

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