Implementation of Continuous Capnography Is Associated With a Decreased Utilization of Blood Gases
Bibliographic record
Abstract
BACKGROUND: Capnography provides a continuous, non-invasive monitoring of the CO2 to assess adequacy of ventilation and provide added safety features in mechanically ventilated patients by allowing for quick identification of unplanned extubation. These monitors may allow for decreased utilization of blood gases. The objective was to determine if implementation of continuous capnography monitoring decreases the utilization of blood gases resulting in decreased charges. METHODS: This is a retrospective review of a quality improvement project that compares the utilization of blood gases before and after the implementation of standard continuous capnography. The time period of April 2010 to September 2010 was compared to April 2011 to September 2011. Parameters collected included total number of blood gases analyzed, cost of blood gas analysis, ventilator and patient days. RESULTS: The total number of blood gases after the institution of end tidal CO2 monitoring decreased from 12,937 in 2009 and 13,171 in 2010 to 8,070 in 2011. The average number of blood gases per encounter decreased from 20.8 in 2009 and 21.6 in 2010 to 13.8 post intervention. The blood gases per ventilator day decreased from 4.94 in 2009 and 4.76 in 2010 to 3.30 post intervention. The total charge savings over a 6-month period was $880,496. CONCLUSIONS: Continuous capnography resulted in a significant savings over a 6-month period by decreasing the utilization of blood gas measurements.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".