Relationship between Obstructive Sleep Apnea (OSA) and Difficult Intubation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: One of the challenges of patients who are candidates for anesthesia is difficult intubation, which leads to severe complications and even death after anesthesia. The aim of this study is to investigate the relationship between obstructive sleep apnea and difficult intubation through systematic review and meta-analysis. METHODS: In this review article, observational articles about the relationship between obstructive sleep apnea and difficult intubation were extracted without time limit by searching national and international databases and the keywords were: difficult intubation, problematic intubation, Intra tracheal-endotracheal, difficult airway OSA, OSAS, obstructive sleep apnea, sleep breathing disorder, anesthesia, and their Persian equivalents. Data were analyzed using meta-analysis and fixed effects model. In order to study the heterogeneity and contradictions in the studies, Q Cochrane and I2 indices were used, respectively. FINDINGS: Of the 72 found articles, 9 articles with a sample size of 1,126 and an average of 125 subjects were included in the study. The results of this study showed that the relationship between obstructive sleep apnea and difficult intubation is significant (OR = 3.88, CI95% = 2.69 – 5.61). In addition, the results of the analysis based on country showed that the highest and lowest odds ratios were observed in studies conducted in France and Canada, respectively. CONCLUSION: The results of this study showed that there is a correlation between obstructive sleep apnea and difficult intubation.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".