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Record W3033301970 · doi:10.1093/ndt/gfaa142.p1516

P1516DEMONSTRATION OF FLUID REMOVAL ACCURACY USING THE SC+ HAEMODIALYSIS SYSTEM

2020· article· en· W3033301970 on OpenAlexaff
Clive Buckberry, Paul Komenda, Keith Heyes, Eric W. Westenbrink, Mark R. Wallace, John Milad

Bibliographic record

VenueNephrology Dialysis Transplantation · 2020
Typearticle
Languageen
FieldEngineering
TopicIntravenous Infusion Technology and Safety
Canadian institutionsUniversity of ManitobaSeven Oaks General Hospital
Fundersnot available
KeywordsCartridgeDialysisUltrafiltration (renal)MedicineBiomedical engineeringHemodialysisFlow (mathematics)Fluid dynamicsSurgeryProcess engineeringMechanical engineeringChromatographyMechanicsEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Background and Aims Fluid management is an integral component for managing patients to correct abnormalities in plasma composition and maintain fluid balance. Consequently, accurate fluid removal during treatment is a critical design element of haemodialysis machines. The SC+ haemodialysis system developed by Quanta Dialysis Technologies, is a small, simple-to-use dialysis system designed to improve patient access to self-care and home haemodialysis. This paper describes the design, evaluation and performance of the flow balance and ultrafiltration module of SC+ to deliver specified fluid removal in accordance with the international technical standards for haemodialysis defined in IEC 60601-2-16 using a number of unique proprietary technologies. Method SC+ uses volumetric flow balancing chambers which are contained within a single use disposable cartridge. During normal operation of SC+, dialysis fluid flows through the cartridge in discrete packets. This is achieved by the application of pneumatic pressure and vacuum to manipulate a flexible PVC membrane that, in turn, opens and closes a sequential series of valves and pump cavities that constitute the flow balance chambers. Proof of system performance was undertaken using a range of dialysers and venous pressures, with and without ultrafiltration, to quantify the net fluid removal error, in order to simulate a range of patient conditions in typical clinical practice. Results In total, the tests comprised 22 separate runs on multiple machines, with 88 individual 30-minute measurement samples, taken over a range of environmental conditions, dialyser types (with differing KuF), dialysate flowrates, and venous pressures. Across all results, there was a mean error of 7.4g/hour rate error, (max allowed is 100g/hr) of positive flow from the blood to the dialysate side with a standard deviation of 19.88g/hr. The maximum permissible rate stated in IEC 60601-2-16 is +/-100g/hr. In specific detail, without ultrafiltration the flow balance error was measured as 2.2g/hr with a deviation of 19.0g/hr, and with ultrafiltration the mean error was 12.8g/hr with a standard deviation of 19.5g/hr. Conclusion It has been demonstrated that flow balance error and fluid removal attainable with the SC+ haemodialysis system lies well within the acceptable standards permitted for haemodialysis machines. This essential performance parameter as defined within IEC 60601-2-16 has been demonstrated across a range of clinically relevant parameters at dialysate flow rates of up to 500ml/min

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.014
GPT teacher head0.219
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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