Identifying Predictors of Airway Complications During Conscious Sedation Procedures
Bibliographic record
Abstract
Conscious sedation procedures are complicated by unanticipated airway compromise and obstruction. The STOP-Bang questionnaire (University of Toronto, 2012) is a validated obstructive sleep apnea screening questionnaire used as a preprocedure evaluation tool to assess a patient's risk for obstructive sleep apnea. The purpose of this study was to determine whether risk factors for obstructive sleep apnea, using the STOP-Bang questionnaire, could predict procedural airway complications in 152 endoscopy patients following conscious sedation. Logistic regression analysis revealed that a STOP-Bang score of greater than 5 (high risk) predicted a 10% change in heart rate (p = .021), apnea (p = .038), and arousal-relieved airway obstruction (p = .023). Every point of increase in body mass index predicted a 10% change in heart rate (p = .046), a drop in oxygen saturation (p = .002), apnea (p = .003), and 1.212 times the odds of requiring arousal-relieved airway obstruction (p = .002). An intermediate-risk STOP-Bang score (3-4) positively correlated to abnormal carbon dioxide values during the procedure (p = .015). These findings concur with existing literature on the topic and translate to clinical considerations of procedural monitoring protocols for patients with a high probability for airway complications during conscious sedation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".