In Vitro Evaluation of a Continuous Potassium Monitoring System in Simulated Dialysis Conditions
Bibliographic record
Abstract
Background: Potassium (K+) dysregulation is a life-threatening condition. During a dialysis session, a patient’s K+ levels often begin above the normal range and end below the normal range, before rebounding after the session. We have developed a continuous K+ monitor (CKM) capable of measuring dynamic K+ levels in real-time. In this report, we mimic dialysis conditions in vitro to characterize the sensors’ ability to measure rapidly shifting K+ levels. Methods: The potentiometric sensor probes consist of a working electrode (WE) that measures K+ directly, selectively, and sensitively and a reference electrode (RE). Eight sensors were immersed in fluid controlled by a pump system able to control K+ levels. We simulated dialysis conditions over 5 hours by changing K+ concentration from 7 mmol/L to 4.5 mmol/L and back to 6 mmol/L. The electrical potential was recorded every 30 seconds, and analyzed by retrofitting to the K+ concentration using a 3 parameters function to account for sensitivity, offset, and drift. Parameters were estimated for each sensor individually using least square fit method. Results: All sensors responded rapidly during K+ changes (Figure). One sensor showed a faulty RE and one showed a time lag of 15 minutes, and were excluded. With the 6 remaining sensors, the average sensitivity was 48 ± 3.5 mV/dec, the drift was 0.84 ± 0.24 mV/h, and the offset was 2.87 ± 14.2 mV. Compared to the programed K+ profile, MARD was <1% and average accuracy was <0.1 mmol/L (defined as the standard deviation of the difference between estimated K+ and reference K+). Conclusion: The CKM sensors responded accurately to changing K+ levels across the physiological range during a simulated dialysis session. In vivo testing is needed to further support these results. Funding: Commercial Support - Proton Intelligence, Inc.Figure: CKM sensor probes tested in a simulated in vitro dialysis session. Profile represents the programmed K+ concentrations.
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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 | 0.000 | 0.001 |
| 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".