Variability in estimating shunt from single pulse oximetry measurements
Bibliographic record
Abstract
Virtual shunt describes the overall loss of O2 content between the alveolar gas and arterial blood. Clinicians indirectly estimate the magnitude of the virtual shunt by monitoring peripheral blood oxygen saturation (SpO2) using non-invasive pulse oximetry. An inherent limitation of this method is the variable precision of pulse oximeters and the non-linear relationship between virtual shunt and SpO2 which is rarely depicted.We propose a model using a combination of basic physiological equations to analyze the estimation of virtual shunt from inspired oxygen (FiO2) and SpO2. The model emphasizes the effect of the non-linearity of the Hb-O2 dissociation curve. Furthermore, it accounts for the variability in SpO2 measurements due to the precision of pulse oximeters.The model was validated with experiments conducted on healthy subjects in a normobaric hypoxia chamber comparing the simultaneous readings from two different commercial pulse oximeters at FiO2 = 21% and 17%. SpO2 probability distributions calculated from the model were estimated. Although a variable bias (1.2-2.1%) in SpO2 between the pulse oximeter brands was observed, the tested pulse oximeters were both within tolerance specified by the manufacturers and matched the probability distributions from the model.The theoretical and experimental findings show that the estimation of virtual shunt is challenging with a single SpO2 measurement using pulse oximeters with tolerances of 2%. Clinical decisions must be based on an appreciation of these limitations.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".