The relationship between upper airway collapse and the severity of obstructive sleep apnea syndrome: A chart review
Bibliographic record
Abstract
BACKGROUND: We sought to determine the ability of the endoscopic Mueller maneuver (MM) to predict the severity of OSAS based on upper airway (UA) collapse. METHODS: This chart review retrospectively analyzed the results of endoscopic Mueller maneuvers examining the UA on 506 patients suspected of having OSAS. There were 3 areas of UA collapse that were evaluated: velopharynx (VP), base of tongue (BOT), and lateral pharyngeal walls (LPW). A sleep study was done after the examination to assess the severity of OSAS based on the apnea-hypopnea index (AHI). RESULTS: A total of 506 patients met criteria for OSAS, with 194 mild cases (5 ≤ AHI < 15), 163 moderate cases (15 ≤ AHI < 30) and 149 severe cases (30 ≤ AHI). At the VP, 30 patients had minimal collapse (mean AHI = 17); 41 patients had moderate VP collapse (mean AHI = 25); 392 patients had severe VP collapse (mean AHI = 27). At the BOT, 144 patients had minimal collapse (mean AHI = 19); 187 patients had moderate BOT collapse (mean AHI = 24); 175 patients had severe BOT collapse (mean AHI = 33). At the LPW, 158 patients had minimal collapse (mean AHI = 20); 109 patients had moderate LPW collapse (mean AHI = 25); 120 patients had severe LPW collapse (mean AHI =33). The correlations found between VP collapse, BOT collapse, and LPW collapse and OSAS severity were: r = 0.069 (95% CI; -0.022, 0.16), r = 0.26 (95% CI; 0.18, 0.34) and r = 0.22 (95% CI; 0.12, 0.31), respectively. CONCLUSIONS: In this study, the degree of collapse of the UA at all levels, especially at the BOT and LPW levels, correlate significantly with the severity of OSAS. The Mueller maneuver helped identify patients with severe sleep apnea based on UA collapse. The MM cannot be used to diagnose OSAS, but can be a valuable tool to help the physician estimate the severity of sleep apnea and the urgency to obtain a sleep study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".