A Randomized Controlled Trial on Facial Muscle Therapy in Nasal Obstruction
Bibliographic record
Abstract
Background: Muscular exercises of the lateral nasal wall have been described as a potential treatment of nasal valve obstruction. The objective of this study was to compare whether nasal exercises improve nasal obstruction, using a randomized controlled model. Methods: Participants were randomized into groups performing exercises targeting nasal (group A) or facial (group B) muscles. Nasal obstruction was measured using a validated standardized patient-reported outcome measure (PROM) questionnaire (Standardized Cosmesis and Health Nasal Outcomes Survey [SCHNOS]) at enrolment and at the end of the 8 weeks program. Results: Fifty-six patients completed the study. Upon completion of the programs, a three-point SCHNOC-C score improvement (95% [confidence interval, CI] = [−9 to 2]) was seen in Group A, whereas an eight-point score improvement (95% [CI] = [−15 to −0.4]) was observed in Group B. A seven-point SCHNOS-O score difference (95% [CI] = [−13 to −1]) was observed in Group A, whereas a difference of 15 points was seen in Group B (95% [CI] = [−22 to −8]). No significant difference was found between group A and B ( p = 0.373 and p = 0.065, respectively). Conclusion: This randomized controlled trial suggested that nasal muscle exercises show no improvement on nasal obstruction.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".