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 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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".