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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".