A Continuous and Noninvasive Method to Estimate Pao 2/Fio 2 Ratio
Bibliographic record
Abstract
OBJECTIVES: To validate a method for continuously estimating the Pa o 2 /F io 2 (PF) ratio in all critically ill pediatric patients using only standard continuous data monitoring. DESIGN: Retrospective study on a high temporal resolution database. SETTING: PICU in Montreal, QC, Canada. PATIENTS/SUBJECTS: We included any patients admitted from May 2015 to May 2023 who had an arterial blood gas (ABG) with concurrent continuous pulsed oximetry saturation (Sp o 2 ) values. We used our previously validated mathematical model to determine the magnitude of hypoxemia by computing the estimated ePa o 2 /F io 2 (ePF) ratio and comparing it to the Sp o 2 /F io 2 (SF), using PF ratio as the reference standard. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We analyzed a total of 20,828 ABGs. When Sp o 2 was below or equal to 97%, the ePF ratio showed a significantly better hypoxemia classification (none, light/moderate, or severe) than the SF ratio (0.80 vs. 0.72; p < 0.001), a lower fixed bias (16.26 vs. –35.24; p < 0.001), a lower mean absolute error (37.92 vs. 63.93; p < 0.001) and a lower proportional bias (slope of 1.01 vs. 0.81; p < 0.001). ePF ratio has also a better limits of agreement difference from Bland-Altman plot (248.10 vs. 292.45; p < 0.001) and coefficient of determination (0.68 vs. 0.59; p < 0.001). When Sp o 2 was above 97%, the ePF ratio had better classification with Kappa (0.53 vs. 0.43; p < 0.001) and lower fixed bias (–0.63 vs. 65.68; p < 0.001). CONCLUSIONS: The PF ratio based on ePF allows for a continuous estimation of hypoxemia severity with a better performance than the SF ratio.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".