0564 Assessment of Tongue and Soft Palate Muscles Mechanical Properties in Patients with OSA
Bibliographic record
Abstract
Abstract Introduction Soft palate muscles are crucial in the maintenance of UA patency. Different contraction tasks have been used to investigate tongue mechanical properties, but not to soft palate muscles. This study aimed to investigate the mechanical consequences of tongue and soft palate muscles fatigue in moderate-severe OSA patients. Methods 12 moderate and 8 severe patients with OSA were enrolled. Measurements include strength, endurance, and fatigue indices. During the soft palate fatiguing protocol, subjects were asked to develop repetitive intra-oral positive pressure during cheek-bulging maneuvers while wearing a mouth piece to keep the jaw opened. Tongue mechanical properties were also assessed using protrusion tasks with similar protocol. Subjects were encouraged to develop sustained maximal bulging pressure or tongue protrusion force for 5 sec every 10 sec until the peak pressure did not reach 85% of baseline maximal pressure for 2 consecutive times. The influence of age and BMI were also investigated. Results The sex, age were not significantly different between the 2 OSA groups. BMI was significantly higher in severe OSA patients (p<0.05). Overall, the tongue maximal voluntary contraction force (MVC), endurance time and total muscle work were respectively positively associated with the ones obtained from the soft palate fatiguing task (rs=0.51, 0.43, 0.66, respectively). The MVC of both tongue and soft palate muscles were positively correlated with BMI in all subjects (rs=0.43, 0.5 respectively). The recovery time from soft palate fatigue was significantly longer in moderate than severe OSA patients (270s ± 192.3s and 120s ± 0, p =0.02). Interestingly, the recovery time was positively correlated with AHI in tongue fatiguing task, while negatively correlated with supine AHI and age in soft palate fatiguing task (p<0.05). In both tasks, MVC was negatively correlated with the endurance time (p<0.05). Conclusion Moderate patients are less likely to recover from soft palate muscle fatigue. A more severe apneic disease is associated with longer recovery time from tongue fatigue, but with shorter recovery time from soft palate fatigue. Our results suggest that alteration in tongue and velopharyngeal muscles function may differ according to the severity of disease. Support SBD from IUCPQ Foundation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".