Optical System Shows Promise for Online Detection of Peritonitis in Peritoneal Dialysis
Bibliographic record
Abstract
Background: Peritoneal Dialysis (PD) is associated with significant patient morbidity and mortality. Prompt peritonitis diagnosis and treatment is crucial and may be limited by the lack of early patient or care partner recognition of peritonitis signs and symptoms thereby delaying clinical presentation and treatment and adversely impacting peritonitis treatment outcomes. Methods: The Intelligent Dialysis Assistant (IDA), a new electronic automated ambulatory PD exchange device (part of liberDi's Digital Dialysis Clinic) provides aseptic PD exchanges. The IDA is fitted with an inline WBC sensor that can transmit online detection of white blood cells (WBC) in the PD effluent and aid in the early diagnosis of peritonitis. To check the capabilities of the sensor in- vitro, we created PD solutions with a range of 150- 16,000 cells/μL to mimic PD effluent peritonitis conditions. Results: The sensor installed in the IDA (part of liberDi's Digital Dialysis Clinic) was able to detect the different concentrations of white blood cells in the solution (from 150 through 16,000 cell/μL), with a high linear correlation (R2 =0.98). Conclusions: A point-of-care testing system for detecting peritonitis using a sensor is a promising approach that may improve the prompt diagnosis and treatment of peritonitis in PD patients. The ability of the installed sensor in the IDA to detect low concentrations and volumes of white blood cells suggests that may be a reliable tool to detect peritonitis in the PD effluent and will require validation via further clinical studies. Funding: Commercial Support - liberDiFigure 1: illustrates how the differences in optical system readings between the infected solution and the reference solution varies with different concentrations of white blood cells for two tested blood samples, as well as the average of those samples. Each point denotes 103 readings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".