Use of a Respiratory Volume Monitor to Assess Respiratory Competence in Cardiac Surgery Patients After Extubation
Bibliographic record
Abstract
BACKGROUND: Patients who have undergone cardiac surgery are generally mechanically ventilated postoperatively. Early postoperative extubation is currently recommended in anesthesia guidelines. No current technology can accurately, non-invasively, measure respiratory competence after extubation. Pulse oximetry has been helpful, but this is a late indicator of respiratory compromise. A novel, non-invasive, respiratory volume monitor (RVM) has been shown to deliver accurate continuous, real-time minute ventilation (MV), tidal volume (TV) and respiratory rate (RR) measurements and provide an objective measure of respiratory competence. The RVM will accurately reflect MV, TV and RR in cardiac surgery patients before and after extubation. METHODS: RVM traces were recorded from patients before and after cardiac surgery. Continuous monitoring began on admission to the unit and was ended at 24 h after extubation. RVM-based MV, TV and RR were calculated from 30-s segments. MV, TV and RR were also continuously recorded from the ventilator prior to extubation. The RVM was calibrated to each patient using the readings from the ventilator. RESULTS: During mechanical ventilation, the RVM measured TVs strongly correlated with the ventilator TVs (r = 0.97). Following extubation, the patient's breathing became more erratic and TVs and MVs decreased. Within 1 h, all patients studied showed a marked recovery of MV and TV. CONCLUSIONS: RVM-based MV, TV and RR correlated well with similar data collected from ventilators. After extubation, RVM shows promise as a means to monitor respiratory competence of non-intubated patients, and has implications for use in other settings and improving patient safety.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".