Noninvasive Estimation of Arterial Co 2 From End-Tidal Co 2 in Mechanically Ventilated Children: The GRAeDIENT Pilot Study*
Bibliographic record
Abstract
OBJECTIVES: The aim of our pilot study was to develop a model to better predict Paco2 in mechanically ventilated children using noninvasive parameters including volumetric capnography. DESIGN: Prospective clinical pilot study. SETTING: Level III PICU. PATIENTS: Sixty-five mechanically ventilated children. INTERVENTIONS: None. MATERIALS AND METHODS: We conducted a prospective clinical pilot study that included all children admitted to the PICU (< 18 yr; weight, > 3 kg; mechanically ventilated, > 6 hr; with an arterial line). A predictive model for PaCO2 was developed using linear multivariable regression. Among the data collected in PICU patients, candidate predictors of PaCO2 were defined by a panel of experts and included end-tidal partial pressure of carbon dioxide, ventilation parameters, and data resulting from the analysis of volumetric capnogram recorded 5 minutes before an arterial blood gas. Children with tidal volume less than 30 mL were excluded because of technical limits. RESULTS: A total of 65 children (43 boys, 65%) (65 [21-150] mo old) were analyzed. By linear multivariable regression, the best model included the mean airway pressure, end-tidal partial pressure of carbon dioxide, FIO2, and the capnographic index with an R equal to 0.90, p value less than 0.001. After correction, 95% (n = 62) of children had an estimated PaCO2 at ± 5 mm Hg. CONCLUSION: Our model developed provides an accurate estimation of the PaCO2 using end-tidal CO2 and noninvasive variables. Studies are needed to validate the equation in PICUs.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".