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Accuracy of Online Conductivity Monitor Compared with Daugirdas (dPVV/Kt/V) Model. Use in the Clinical Field

2004· article· en· W1778297030 on OpenAlexvenueno aff
Secundino Cigarrán, Francisco Coronel, J. Torrente, M. Sevilla, J.C.D. Baylón

Bibliographic record

VenueHemodialysis International · 2004
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDialysisKt/VHemodialysisDialysis adequacySurgeryInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

The influence of dialysis prescription on outcome is well established. Hemodialysis dose has been shown to have a distinct impact upon the morbidity and mortality rate in patients on regular treatment therapy. Hence, adequacy of dialysis should be guaranteed. New devices based in online conductivity measures have been developed to achieve an adequate dialysis dose. The aim of this study was to compare online conductivity monitor (OCM) with dialysis dose standard methods (Daugirdas Kt/V). 24 anuric patients were included in a cross‐sectional study: 20.8% were diabetics, mean age 64.7 ± 18.2 years; 16% females. Access blood were AVFi and the effective dialyzed blood flow was set at 350 mL/min, with recirculation <5%. BMI was 25.4 ± 3.8 kg/m2 and body weight was 69.7 ± 12 kg. All patients were dialyzed thrice weekly (245 ± 21 min) with dialysis machine 4008H (Fresenius Medical Care) equipped with OCM monitor and the hollow fiber high‐flux polysulfone membrane (HF‐80 1.8 m2) and helixone (Fx‐60, 1.6 m2). Dialysate flow was maintained at 500 mL/min, with standard dialysate liquid. Drug therapy was not varied. Each patient was subjected to OCM on midweek day, during 3 consecutive weeks on the same day that blood Kt/V samples were taken as well. Each patient got 3 OCM measurements and Kt/V samples. Data were processed and statistically analyzed with SPSS 11.0 software package. Kt/V OCM relation to other baseline characteristics was assessed by using contingency tables, t‐tests, analysis of variance, and linear regression, as appropriate. All the tests were performed for a 0.05 significance level. Descriptives (n = 24) Mean SD Age (year) 64.75 18.243 Dry weight 69.7125 12.01178 Interdialysis weight gain 2266.67 1016.673 BMI 25.4155 3.83630 HD time 245.21 21.340 OCM 1.29921 0.201072 Daugirdas Kt/V 1.3287 0.21043 Watson volume (L) 36.833 6.3095 Linear regression analysis: Kt/V OCM relationship Variable Regression coefficient (β) r2 p Age (year) −0.631 0.398 <0.001 Daugirdas Kt/V −0.981 0.962 <0.001 BMI −0.327 0.107 NS Watson volume (L) −0.833 0.694 <0.001 The OCM option correlates well with Daugirdas Kt/V obtained from blood samples, in 96.2%, and provides a safe and accurate tool for hemodialysis, adding efficiency to dialysis adequacy monitoring in clinical practice. Also, OCM allows for an individualized dialysis dose. Furthers studies are required to evaluate its influence on patients' evolution.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.086
GPT teacher head0.379
Teacher spread0.292 · 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 designObservational
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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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