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Record W1840350818 · doi:10.1186/s12882-015-0144-z

The effect of citrate dialysate on intradialytic heparin dose in haemodialysis patients: study design of a randomised controlled trial

2015· article· en· W1840350818 on OpenAlexaff
Davina J. Tai, Kelvin Leung, Pietro Ravani, Robert R. Quinn, Nairne Scott‐Douglas, Jennifer M. MacRae

Bibliographic record

VenueBMC Nephrology · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsAlberta Health ServicesUniversity of CalgaryUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineHeparinAnticoagulantDialysisHemodialysisAdverse effectInternal medicineRandomized controlled trialNephrologySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Unfractionated heparin is the most common anticoagulant used in haemodialysis (HD), although it has many potential adverse effects. Citrate dialysate (CD) has an anticoagulant effect which may allow reduction in cumulative heparin dose (CHD) compared to standard acetate dialysate (AD). METHODS: This double-blinded, randomised, cross-over trial of chronic haemodialysis patients determines if CD allows reduction in CHD during HD compared with AD. After enrolment, intradialytic heparin is minimised during a two-week run-in period using a standardised protocol based on a visual clotting score. Patients still requiring intradialytic heparin after the run-in period are randomised to two weeks of HD with AD followed by two weeks of CD (Sequence 1) or two weeks of HD with CD followed by two weeks of AD (Sequence 2). The primary outcome is the change in CHD with CD compared with AD. Secondary outcomes include metabolic and haemodynamic parameters, and dialysis adequacy. DISCUSSION: This randomised controlled trial will determine the impact of CD compared with AD on CHD during HD. TRIAL REGISTRATION: ClinicalTrials.gov NCT01466959.

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)0.023
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.026
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0120.005
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0080.004
Insufficient payload (model declined to judge)0.0150.002

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 head0.046
GPT teacher head0.334
Teacher spread0.288 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations6
Published2015
Admission routes1
Has abstractyes

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