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Record W2095512921 · doi:10.1159/000363049

What Is Single Needle Cannulation Hemodialysis: Is It Adequate?

2014· article· en· W2095512921 on OpenAlexafffund
Shih‐Han S. Huang, Sachin S Shah, A. B. R. Thomson, Sally Laporte, Guido Filler, Robert M. Lindsay

Bibliographic record

VenueBlood Purification · 2014
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsLondon Health Sciences CentreWestern University
FundersLondon Health Sciences Centre
KeywordsHemodialysisMedicineUreaBlood flowUrologySurgeryNuclear medicineChemistryInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: It is important to know the relative clearances obtained when using single-needle versus double-needle cannulation techniques. METHOD: Twelve hemodialysis treatments were conducted using a machine that is capable of single-needle as well as double-needle cannulation. Single-needle and double-needle blood flow rates, as well as urea clearance, were compared. RESULTS: The measured blood flow rates were 368 ± 11 ml/min, 294 ± 4 ml/min, 200 ± 0 ml/min, and 100 ± 0 ml/min during double-needle hemodialysis and were 201 ± 10.9 ml/min, 173 ± 44.9 ml/min, 103 ± 4.1 ml/min, and 45 ± 4.9 ml/min during single-needle hemodialysis. The hemodialysis urea clearances at similar blood flow rate (approximately 200 ml/min) were 167 ± 4 ml/min and 161 ± 9 ml/min (paired t test; p > 0.05), respectively. CONCLUSION: The measured blood flow rates and urea clearances during single-needle hemodialysis were approximately half of the measured blood flow rate during double-needle hemodialysis, and should be used in selected settings.

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)0.016
metaresearch head score (Gemma)0.061
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.000
Research integrity0.0040.002
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 head0.025
GPT teacher head0.261
Teacher spread0.236 · 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

Citations12
Published2014
Admission routes2
Has abstractyes

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