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MMA: The Fight Against Sleep Apnea

2022· article· en· W4225404993 on OpenAlexaff
Kody M. Wolfstadt, Corey Smith, Michael Shimizu, Ali Tassi, Louis M. Ferreira, Jeff McQuade, Timothy D. Wilson

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineAirwayObstructive sleep apneaAnesthesiaVentilation (architecture)Sleep apneaPositive airway pressureOrthodonticsSurgery

Abstract

fetched live from OpenAlex

Introduction Affecting approximately one billion people worldwide, obstructive sleep apnea (OSA) occurs when an individual’s airway self‐obstructs during sleep. Persons suffering OSA are generally less healthy and are more likely to develop a myriad of conditions known collectively as Metabolic Syndrome. One OSA solution is maxillomandibular advancement surgery (MMA), involving maxillomandibular complex (MMC) repositioning. While the surgery reports an 87‐100% success rate, the mechanisms of how MMA reduces OSA is not as clear. Further, a proportion of patients are dissatisfied with their appearance after the procedure. This project aims to simulate ventilation in cadavers who have undergone MMA surgery using an incremental MMA approach to measure airway resistances and relate these changes to resulting facial alteration. Surgery The MMA procedures were performed by the same dental surgeon. The oral distraction devices (KLS Martin, Florida) were left intact for the entirety of the experiment for manual jaw advancements. For each dependent variable, the MMC was advanced from 0mm to 14mm in 2mm increments. Ventilation A patient ventilator (LTV 1000 Pulmonetics, Minnesota) simulated ventilation at each MMA increment. Tidal volumes (TV) were calculated for each cadaver at 6ml/kg of body mass, over the same breathing frequency (12 breaths/minute), resulting in constant air flow rates. At each advancement of the MMC, airway resistance (R) is calculated using breath‐by‐breath analysis of peak inspiratory pressure and plateau pressure at each flow rate. A minimum of 10 breaths were used to calculate R at each MMA increment. Facial Scans After every ventilation condition, topographical scans measured 3D changes in the face (Space Spider Scanner, Artec, California). Scan areas of interest extended from hyoid to infraorbital foramen and to the tragi, laterally. The 3D meshes enable calculation of discreet skin surface alterations at each MMA increment and comparisons to baseline topography. Comparing incremental changes to baselines as percentages, a facial alteration index (∆F%) allows comparisons across individuals. Discussion Determination of cadaveric breath‐by‐breath airway forces during MMA is novel and ongoing. Preliminary results (n=1, F, 27yrs, 46kg) demonstrate inverse relationships between incremental MMA and R. With each 2mm MMA, R decreased an average of 4.3 cmH2O/L/s (r = ‐0.82). The foundation of the change in R is supported by an average decrease in peak inspiratory pressure of 3.67 cmH2O (r = ‐0.95) and an average decrease in plateau pressure of 2.81 cmH2O (r = ‐0.97) with each 2mm MMA. Use of ∆F% analysis enables demonstration of the relationship between MMA, airway resistance, and resultant facial alteration. The combined approaches are hypothesized to predict relationships between airway resistance and facial alteration at each level of MMA. Determination of this relationship will be a powerful tool, enabling surgeon and patient to be involved in informed decision‐making.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0180.005

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 head0.022
GPT teacher head0.279
Teacher spread0.257 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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