Validation of Polar Elixir™ Pulse Oximeter against Arterial Blood Gases during Stepwise Steady-State Inspired Hypoxia
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the accuracy of peripheral oxygen saturation (SpO 2 ) measurements from Polar Elixir™ pulse oximetry technology compared with arterial oxygen saturation (SaO 2 ) measurements during acute stepwise steady-state inspired hypoxia at rest. A post hoc objective was to determine if SpO 2 measurements could be improved by recalibrating the Polar Elixir™ algorithm with SaO 2 values from a random subset of participants. METHODS: The International Organization for Standardization (ISO) protocol (ISO 80601-2-61:2017) for evaluating the SpO 2 accuracy of pulse oximeter equipment was followed whereby five plateaus of SaO 2 between 70% and 100% were achieved using stepwise reductions in inspired O 2 during supine rest. Blood samples drawn through a radial arterial catheter from 25 participants were first used to compare SaO 2 with SpO 2 measurements from Polar Elixir™. Then the Polar Elixir™ algorithm was recalibrated using SaO 2 data from 13 random participants, and SpO 2 estimates were recalculated for the other 12 participants. For SaO 2 values between 70% and 100%, root mean square error, intraclass correlation coefficients (ICC), Pearson correlations, and Bland-Altman plots were used to assess the accuracy, agreement, and strength of relationship between SaO 2 values and SpO 2 values from Polar Elixir™. RESULTS: The initial root mean square error for Polar Elixir™ was 4.13%. After recalibrating the algorithm, the RMSE was improved to 2.67%. The ICC revealed excellent levels of agreement between SaO 2 and Polar Elixir™ SpO 2 values both before (ICC(1,3) = 0.837, df = 574, P < 0.001) and after (ICC(1,3) = 0.942, df = 287, P < 0.001) recalibration. CONCLUSIONS: Relative to ISO standards, Polar Elixir™ yielded accurate SpO 2 measurements during stepwise inspired hypoxia at rest when compared with SaO 2 values, which were improved by recalibrating the algorithm using a subset of the SaO 2 data.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".