MétaCan
Menu
Back to cohort
Record W7105213230 · doi:10.17605/osf.io/4m5kj

Lock Solutions for Central Venous Catheters in Hemodialysis: A Comprehensive Systematic Review and Meta-Analysis

2025· other· W7105213230 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCatheterVascular accessObservational studyHemodialysisLock (firearm)Adverse effectRandomized controlled trialCentral venous catheter

Abstract

fetched live from OpenAlex

Central venous catheters (CVCs) are widely used in chronic hemodialysis, especially in patients lacking long-term vascular access. However, they are associated with significant risks, including catheter-related bloodstream infections (CRBSIs) and catheter dysfunction. Lock solutions—such as heparin, citrate, taurolidine, ethanol, and antibiotic-based formulations—are used between dialysis sessions to prevent these complications, but their comparative effectiveness and safety remain uncertain. This project is a systematic review and meta-analysis of randomized and observational studies evaluating lock solutions for tunneled and non-tunneled CVCs in hemodialysis patients. The primary aim is to compare their impact on CRBSI incidence and catheter patency. Secondary outcomes—such as adverse events, hospitalization, and mortality—are grouped into a miscellaneous outcome table for structured reporting and synthesis. Risk of bias will be assessed using RoB 2 and the Newcastle-Ottawa Scale. A random-effects meta-analysis will be performed, with subgroup and sensitivity analyses based on lock type, study design, and patient characteristics. The findings aim to inform clinical guidelines and promote safer, more effective catheter care in dialysis.

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)0.011
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0180.004
Bibliometrics0.0040.030
Science and technology studies0.0020.003
Scholarly communication0.0030.001
Open science0.0110.004
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 head0.092
GPT teacher head0.371
Teacher spread0.279 · 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 designMeta-analysis
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueOpen Science FrameworkFrench-language works237,207