Characterization of respiratory compromise and the potential clinical utility of capnography in the post-anesthesia care unit: a blinded observational trial
Bibliographic record
Abstract
The utility of capnography to detect early respiratory compromise in surgical patients after anesthesia is unclear due to limited prospective data. The purpose of this trial was to determine the frequency and duration of capnography-detected respiratory adverse events in the post-anesthesia care unit (PACU). In this prospective observational trial, 250 consenting patients undergoing elective surgery with general anesthesia were monitored by standard monitoring together with blinded capnography and pulse oximetry monitoring. The capnography notification settings were adjusted to match nursing (Level II) and physician (Level I) alarm thresholds. 163 (95%) patients had a Level II notification and 135 (78%) had a Level I notification during standard monitoring. The most common events detected by the capnography monitor included hypocapnia, apnea, tachypnea, bradypnea and hypoxemia, with silent notification duration for these events ranging from 17 ± 13 to 189 ± 127 s. During standard monitoring, 15 respiratory adverse events were reported, with 8 events occurring when valid blinded/silenced capnography and pulse oximetry data was collected simultaneously. Capnography and the Integrated Pulmonary Index™ algorithm (IPI) detected respiratory adverse events earlier than standard monitoring in 75% and 88% of cases, respectively, with an average early warning time of 8 ± 11 min. Three patients' blinded capnography was unblinded to facilitate clinical care. Respiratory adverse events are frequent in the PACU, and the addition of capnography and IPI to current standard monitoring provides potentially clinically relevant information on respiratory status, including early warning of some respiratory adverse events.Trial registration ClinialTrials.gov Identifier NCT02707003 (https://clinicaltrials.gov/ct2/show/NCT02707003).
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".