O-212 Non-invasive Estimation Of The Paco2 With Volumetric Capnography In Children Mechanically Ventilated
Bibliographic record
Abstract
Background In paediatric intensive care unit (PICU), the relationship between end-tidal partial pressure of carbon dioxide (PetCO2) and arterial partial pressure of carbon dioxide (PaCO2) may vary dramatically (PetCO2-PaCO2 difference between -36 and 63 mmHg) (1). The aim of our study was to develop a model using volumetric capnography (VolCap) to better predict PaCO2 in mechanically ventilated children. Material and methods We conducted a prospective clinical study that included all children admitted at Ste-Justine hospital, age 3 kg, mechanically ventilated > 12 h, with an arterial cannula. After literature review, we collected specific data from medical record including demographic data, clinical informations, ventilation, VolCap (NM3, Respironics, Philips, USA) and biological parameters. VoCap was recorded 15 min before an arterial blood gas and analysed breath-by-breath using a specific software (FlowTool, Philips, USA). The predictive model for PaCO2 was developed using a linear multivariable regression with the best determination coefficient (R2). Results 43 children (26 boys, 60%) age of 52 [9–137] months were included. Children with Tidal volume less than 30 ml were excluded because of technical bias in VolCap interpretation by the software. In linear multivariable regression, the best model included the mean airway pressure (p = 0.01), PetCO2 (p2 (p = 0.014) and the capnographic index (100*Slope SIII/Slope SII) (p = 0.003) with a R2= 0.85. Conclusion Our preliminary results show that VoCap can help to improve the non-invasive estimation of PaCO2. Further research is necessary to validate the accuracy of our model. Reference McDonaldet al. Pediatr Crit Care Med 2002;3:244-249
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".