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Incremental Maxillomandibular Advancement: The Relationship Between Skeletal Advancement and Airway Volume

2019· article· en· W3176728804 on OpenAlexaff
Moulik Patel, Jenna J. Yuen, Michael Shimizu, Tyler S. Beveridge, Ali Tassi, Khadry Galil, Timothy D. Wilson

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsAirwayMedicineObstructive sleep apneaBreathingCraniofacialOrthodonticsAnesthesia

Abstract

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Introduction Obstructive sleep apnea (OSA) is characterized by repetitive episodes of supra‐laryngeal airway collapse during sleep, causing increased airflow resistance and reduced respiration. Obstruction and reduced volume mainly occurs in the retropalatal (RPS) and/or retroglossal space (RGS). The most effective surgical treatment (save tracheostomy) is maxillomandibular advancement (MMA). During MMA, the skeletal framework is modified, thereby reducing airway collapse upon inspiration. A clear mechanistic understanding of MMA surgery describing airway dimensions and resulting volumetric increases does not yet exist. The primary objective of the study was to define volume changes during incremental MMA. The secondary objective was to measure where maximal airway tissue movement occurs. Methods An oral surgeon performed MMA on three fresh cadaveric heads (n=3). The mandible and maxilla were advanced by four implanted distraction devices (KLS Martin Inc.). The devices were bilaterally advanced by 2mm increments to 14mm to encompass the full range of clinical advancements. Computerized tomography (CT) scans, (0.6 mm isotropic, Siemens, O‐arm), were taken at baseline (no advancement) and each advancement level (2, 4, 6, 8, 10, 12, 14mm). For the primary objective, total airway volume and linear anteroposterior (AP) and lateral (LAT) 2D dimensions of the RPS and RGS were analyzed at each incremental advancement using Amira™ (Thermo Fisher Scientific). To address the secondary objective, translation of airway tissues, radiopaque microbeads (800 μm, Ortech) were implanted at borders of the RPS and RGS using a syringe and endoscope and their movement was tracked through each advancement. Results During incremental MMA, airway volume increased at each increment compared to baseline. For 0–2, 2–4 & 4–6mm, normalized volume increases were incrementally larger with each advancement [mean 0–2mm = 20.3 ± 22.8%; mean 2–4mm = 34.9 ± 13.8%; mean 4–6mm = 62.4 ± 17.2%]. Consistent volumetric increases occurred during the advancements from 6mm to 10mm [mean 6–8mm = 50.8 ± 27.0%; mean 8–10mm = 45.6 ± 28.7%]. Preliminary analysis (n=1) shows that during early MMA (2mm–6mm), lateral dimensions increased at a greater magnitude compared to later advancements (8–14mm). Microbead migration supports these findings while highlighting airway wall movement. Conclusion While volumetric increases occur at each advancement, the greatest relative change occurs at the advancement from 4–6mm. Relative volumetric increases show a consistent increase in airway volume with each incremental advancement throughout 6–10mm. Lateral wall widening is the chief mechanism for early volume change, at advancements between 2–6mm, while AP dimensions are affected at 8–14mm advancements. The relationships between 2D (AP/LAT) changes and 3D (volume) demonstrate that volume change in initial advancements are primarily due to lateral enlargement. Depending on OSA severity, clinicians may exploit these relationships when performing MMA. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.001
metaresearch head score (Gemma)0.002
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.251
Teacher spread0.239 · 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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Citations0
Published2019
Admission routes1
Has abstractyes

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