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Interdialytic weight gain and ultrafiltration rate in hemodialysis: Lessons about fluid adherence from a national registry of clinical practice

2009· article· en· W1971609592 on OpenAlexvenueno aff
Magnus Lindberg, Karl‐Göran Prütz, Per Lindberg, Björn Wíkström

Bibliographic record

VenueHemodialysis International · 2009
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersSigne och Olof Wallenius Stiftelse
KeywordsMedicineHemodialysisWeight gainDialysisDialysis adequacyBody mass indexInternal medicineIntensive care medicineBody weight

Abstract

fetched live from OpenAlex

Excessive interdialytic weight gain (IWG) and ultrafiltration rates (UFR) above 10 mL/h/kg body weight imply higher morbidity and mortality. This study aimed to estimate the prevalence of high fluid consumers, describe UFR patterns, and describe patient characteristics associated with IWG and UFR. The Swedish Dialysis DataBase and The Swedish Renal Registry of Active Treatment of Uremia were used as data sources. Data were analyzed from patients aged >/=18 on regular treatment with hemodialysis (HD) and registered during 2002 to 2006. Interdialytic weight gain and dialytic UFR were examined in annual cohorts and the records were based on 9693 HD sessions in 4498 patients. Differences in proportions were analyzed with the chi-square test and differences in means were tested using the ANOVA or the t test. About 30% of the patients had IWG that exceed 3.5% of dry body weight and 5% had IWG >/=5.7%. The volume removed during HD was >10 mL/h/kg for 15% to 23% of the patients, and this rate increased during the first dialytic year. Patient characteristics associated with fluid overload were younger age, lower body mass index, longer dialytic vintage, and high blood pressure. By studying IWG and dialytic UFR as quality indicators, it is shown that there is a potential for continuing improvement in the care of patients in HD settings, i.e., to enhanced adherence to fluid restriction or alternatively to extend the frequency of dialysis for all patients, e.g., by providing daily treatment.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.372
Teacher spread0.335 · 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.

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

Citations52
Published2009
Admission routes1
Has abstractyes

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