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Record W2415220936 · doi:10.1177/039139880102401002

Prevention of Catheter Related Infections in Patients on CAPD

2001· article· en· W2415220936 on OpenAlexaff
Elias Thodis, Ploumis Passadakis, Vassilis Vargemezis, Dimitrios G. Oreopoulos

Bibliographic record

VenueThe International Journal of Artificial Organs · 2001
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMupirocinMedicineCatheterPeritoneal dialysisExit sitePeritonitisCarriageSurgeryIntensive care medicineStaphylococcus aureusAdverse effectInternal medicineMethicillin-resistant Staphylococcus aureusPathology

Abstract

fetched live from OpenAlex

Catheter-related infections remain a serious problem for patients on peritoneal dialysis. Such infections can be reduced by careful patient selection and training, by the use of the best connection technology and screening and treating nasal carriage. To date, treatment is less than optimal and therefore, the primary goal should be prevention of catheter-related infections. Prevention is based on improving catheter design and implantation technique, while providing careful exit-site care. Regardless of how it is implemented, we must aggressively pursue the prevention of catheter-related infections by eradicating S. aureus exit-site carriage in PD patients. Based on its effectiveness in adult PD patients, its low rate of adverse effects, and its reasonable cost-effectiveness, application of mupirocin ointment at the exit-site is the current method of choice for preventing PD catheter infections caused by S. aureus. In addition to reducing S. aureus exit-site infections, mupirocin seems to reduce the rates of staphylococcal peritonitis and PD catheter loss. Whether the ointment should be applied in the nares, to the exit-site or both, and whether it should be used only in staphylococcal nasal carriers or all PD patients requires further study.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.033
GPT teacher head0.354
Teacher spread0.321 · 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 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

Citations13
Published2001
Admission routes1
Has abstractyes

Explore more

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