Capnography Improves Detection of Apnea During Procedural Sedation for Percutaneous Transhepatic Cholangiodrainage
Bibliographic record
Abstract
BACKGROUND: Capnography provides noninvasive monitoring of ventilation and can enable early recognition of altered respiration patterns and apnea. OBJECTIVE: To compare the detection of apnea and the prediction of oxygen desaturation and hypoxemia using capnography versus clinical surveillance during procedural sedation for percutaneous transhepatic cholangiodrainage (PTCD). METHODS: Twenty consecutive patients scheduled for PTCD were included in the study. All patients were sedated during the procedure using midazolam and propofol. Aside from standard monitoring, additional capnographic monitoring was used and analyzed by an independent observer. RESULTS: The mean (± SD) cumulative duration of apnea demonstrated by capnography was significantly longer than the mean cumulative duration of clinically detected apnea (207.5 ± 348.8 s versus 8.2 ± 17.9 s; P=0.015). The overall number of detected episodes of apnea was also significantly different (113 versus seven; P=0.012). There were 15 events of oxygen desaturation (decrease in oxygen saturation [SaO2] ≥ 5%), which were predicted in eight of 15 cases by capnography and in one of 15 cases by clinical observation. There were three events of hypoxemia (SaO2 <90%) that were predicted in three of three cases by capnography and in one of three cases by clinical observation. CONCLUSION: Capnographic monitoring was superior to clinical surveillance in the detection of apnea and in the prediction of oxygen desaturation during procedural sedation for PTCD.
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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 | 0.009 |
| 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 | 0.000 | 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".