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Extravascular lung water index: A new method to determine dry weight in chronic hemodialysis patients

2006· article· en· W2043579814 on OpenAlexvenueno aff
C. Kühn, Andrea V. Kühn, Kai RYKOW, B Osten

Bibliographic record

VenueHemodialysis International · 2006
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHemodialysisLungIntensive care medicineIndex (typography)Internal medicine

Abstract

fetched live from OpenAlex

To assess the dry weight of chronic hemodialysis (HD) patients, the extravascular lung water index (ELWI) as a volume parameter was investigated to identify fluid overload. Forty-two patients (30 males, 12 females) with a mean age of 55.7+/-13.0 years who were clinically not overhydrated were connected to the PiCCO system before starting HD treatment. We determined ELWI (normal range 3-7 mL/kg) and the following parameters: global end-diastolic volume index (GEDI, normal range 680-800 mL/m(2)) and intrathoracic blood volume index (ITBI, normal range 850-1000 mL/m(2)) before and after HD to assess the volume status. Brain natriuretic peptide (BNP), aldosterone, and renin as vasoactive hormones were measured at the beginning and at the end of HD treatment as well. In 28 of the 42 patients (67%), elevated values of ELWI were found, indicating interstitial volume overload. There were significant correlations between ELWI and cardiac function index (p=0.003; Pearson's coefficient -0.451), global ejection fraction (p=0.012; Pearson's coefficient -0.389), ITBI (p=0.004; Pearson's coefficient 0.437), and GEDI (p=0.004; Pearson's coefficient 0.437). No significant relations among ELWI and mean arterial pressure (MAP), BNP, aldosterone, and renin were found. In conclusion, the use of ELWI is safe in chronic HD patients and identifies fluid-overloaded patients, who show no obvious signs of hypervolemia. The determination of ELWI is an excellent method to quantify the exact volume in chronic HD patients.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.287
Teacher spread0.278 · 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 teacher head, not a consensus.

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

Citations13
Published2006
Admission routes1
Has abstractyes

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