Postoperative Changes in Sleep-disordered Breathing and Sleep Architecture in Patients with Obstructive Sleep Apnea
Bibliographic record
Abstract
BACKGROUND: Anesthetics, analgesics, and surgery may profoundly affect sleep architecture and aggravate sleep-related breathing disturbances. The authors hypothesized that patients with preoperative polysomnographic evidence of obstructive sleep apnea (OSA) would experience greater changes in these parameters than patients without OSA. METHODS: After obtaining approvals from the Institutional Review Boards, consented patients underwent portable polysomnography preoperatively and on postoperative nights (N) 1, 3, 5, and 7 at home or in hospital. The primary and secondary outcome measurements were polysomnographic parameters of sleep-disordered breathing and sleep architecture. RESULTS: Of the 58 patients completed the study, 38 patients had OSA (apnea hypopnea index [AHI] >5) with median preoperative AHI of 18 events per hour and 20 non-OSA patients had median preoperative AHI of 2. AHI was increased after surgery in both OSA and non-OSA patients (P < 0.05), with peak increase on postoperative N3 (OSA vs. non-OSA, 29 [14, 57] vs. 8 [2, 18], median [25th, 75th percentile], P < 0.05). Hypopnea index accounted for 72% of the postoperative increase in AHI. The central apnea index was low (median = 0) but was significantly increased on postoperative N1 in only non-OSA patients. Sleep efficiency, rapid eye movement sleep, and slow-wave sleep were decreased on N1 in both groups, with gradual recovery. CONCLUSIONS: Postoperatively, sleep architecture was disturbed and AHI was increased in both OSA and non-OSA patients. Although the disturbances in sleep architecture were greatest on postoperative N1, breathing disturbances during sleep were greatest on postoperative N3.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".