Bibliographic record
Abstract
Failure of upper airway muscles to develop efficient dilating forces plays a key role in the occurrence of obstructive sleep apnoea in given patients. Thus, myofunctional therapy has been developed to improve the activity/efficacy of the upper airway (UA) dilator muscles, reduce its fatigability and improve mechanical performance. Various programmes, differing in the types of daytime exercises to be completed, as well as in their duration and intensity, have been evaluated. Meta-analysis confirmed the efficacy of myofunctional therapy, with mean apnoea hypopnoea index (AHI) scores decreasing from 28.0 ± 16.2/h to 18.6 ± 13.1/h, and lowest oxygen saturation (LSAT) values improving from 83.2% ± 6.1% to 85.1% ± 7.0%. In children, MT and nasal washing may result in little to no difference in AHI. Integrating oropharyngeal exercises with the use of a smartphone application to complete and record exercise performances represents an innovative turn in the development of ambulatory MT programmes. Since adherence to therapy is a weakness in conventional OSA strategies such as CPAP, this approach to MT is promising, as evidenced by a 90% mean adherence to it after 3 months of using a smart application. There is further need to determine the most effective combination of exercise algorithms and identify the target population most likely to benefit from MT in outpatient training programmes.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".