Patient outcomes after soft palate implant placement for treatment of snoring.
Bibliographic record
Abstract
BACKGROUND: Multiple options are available for the treatment of snoring. Our objective was to evaluate a palatal implant system in the treatment of snoring caused specifically by retrovelar collapse. STUDY DESIGN: Prospective long-term study comparing snoring outcomes pre- and post-soft palate implantation. METHOD: Snoring patients without significant sleep apnea were offered palatal implantation after assessment via strict inclusion/exclusion criteria. Snoring severity was rated by the bed partner, in a longitudinal fashion, using a Likert scale both in the preoperative and postoperative settings. Paired Student t-tests were used to compare the mean snoring severity preoperatively and at different points of time postoperatively up to 1 year and to compare patient's body mass indices over the study timeline. RESULTS: Data were obtained from 25 patients over a follow-up time of 1 year, for a total of 75 implants. A statistically and clinically significant improvement in the snoring was noted over the 52-week time period of the study in our patient population (mean preoperative score = 9.5, mean 52-week postoperative score = 5.0; p < .001). Body mass index did not significantly change over the duration of the study. CONCLUSION: In our patient population, soft palate implantation was a safe and effective technique for achieving a subjective improvement in the intrusiveness of snoring as noted by the bed partner.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".