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Record W2386992557

The influence of different types of PICC lines on the incidence of complications:a systematic review

2013· article· en· W2386992557 on OpenAlexaboutno aff
Cen Lin

Bibliographic record

VenueZhonghua huli zazhi · 2013
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCochrane LibraryRandomized controlled trialIncidence (geometry)CatheterMeta-analysisInclusion and exclusion criteriaSurgeryCohort studyOcclusionMEDLINEInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective To evaluate the influence of tip designs,materials and diameters of PICC lines on the incidence of PICC-related complications.Methods The relevant randomzed controlled trials,quasi-randomized controlled trials,case-control studies,cohort studies were searched in the Cochrane Library,JBI Library and the databases of MEDLINE,EMbase,CBM and CNKI.The studies were selected according to the inclusion and exclusion criteria by two reviewers,and the quality of included studies was evaluated according to Cochrane Handbook and Newcastle-Ottawa Scale.Meta-analysis was conducted by using RevMan 5.1.Results One randomized controlled trial,three quasi-randomized controlled trials,one case-control study and five cohort studies involving 1996 patients were included.The results of meta-analysis showed that the open-end catheter was associated with a significantly higher incidence of catheter occlusion than Groshong catheter [OR=0.36,95%CI(0.19,0.69),P= 0.002].Polyurethane catheters had a significantly higher incidence of complications than silicone catheters [OR=0.24,95% CI(0.07,0.85),P =0.03 ].Conclusion Groshong catheters have an advantage in reducing catheter occlusion than open-end catheters.Catheter maintenance should be crucial when using polyurethane catheters to avoide complications.Minimum inner diameter catheters should be used to reduce incidence of thrombophlebitis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.344
Teacher spread0.306 · 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 designSystematic review
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

Citations0
Published2013
Admission routes1
Has abstractyes

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