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Record W2790911472 · doi:10.1111/sdi.12688

When is more frequent hemodialysis beneficial?

2018· review· en· W2790911472 on OpenAlexaff
Rita S. Suri, Alan S. Kliger

Bibliographic record

VenueSeminars in Dialysis · 2018
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineAdverse effectHemodialysisLeft ventricular hypertrophyInternal medicineRandomized controlled trialCohortRenal functionCardiologyBlood pressureQuality of life (healthcare)

Abstract

fetched live from OpenAlex

The use of frequent hemodialysis (HD) is growing, with the hope of improving outcomes in end-stage renal disease. We narratively review the three randomized trials, 15 comparative cohort studies, and several case series of frequent HD that empirically demonstrate the potential efficacy and adverse effects of these regimens. Taken together, the randomized studies suggest frequent HD may result in left ventricular mass regression. This effect is most pronounced when left ventricular mass is abnormal, but attenuated by significant residual urine output. Both frequent short and long HD consistently improved blood pressure control and reduced antihypertensive use, despite greater weekly interdialytic weight gains. Serum phosphate was lowered. Frequent short daytime HD improved health-related quality of life, while frequent long overnight HD did not. Regarding adverse effects, frequent HD patients underwent significantly more procedures to salvage arteriovenous vascular accesses. An absolute increase in hypotensive episodes was observed with frequent short HD, while frequent long HD accelerated residual renal function loss and increased perceived caregiver burden. The effect of frequent HD on mortality is controversial, due to conflicting results and limitations of published studies. Finally, pregnancy outcomes may be substantially better with frequent long HD. On the basis of these data, we suggest frequent HD is most likely to benefit patients with left ventricular hypertrophy particularly if there is minimal urine output, those unable to attain dry weight on a thrice weekly schedule, and pregnant women. All patients receiving frequent HD should be advised of and monitored for potential risks.

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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.031
GPT teacher head0.326
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2018
Admission routes1
Has abstractyes

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