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Record W2891007103 · doi:10.3747/pdi.2017.00237

Characteristics and Outcomes of Exit Sites of Buried Peritoneal Dialysis Catheters: A Cohort Study

2018· article· en· W2891007103 on OpenAlexaff
Vaibhav Keskar, Mohan Biyani, Brian Blew, Jeff Warren, Brendan McCormick

Bibliographic record

VenuePeritoneal Dialysis International · 2018
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineExit sitePeritoneal dialysisCatheterDialysisSurgeryCohortInternal medicine

Abstract

fetched live from OpenAlex

Buried peritoneal dialysis (PD) catheters are placed months before dialysis is needed and the exit site is created when the catheter is dissected out at the initiation of dialysis. In contrast, the exit site of an unburied catheter is created by the surgeon at the time of insertion. We reviewed all patients who initiated PD at our center over a 2-year period. At each clinic visit, exit sites were subjectively classified into standard predefined groups. Outcomes of interest were the frequency of perfect exit sites at 2, 6, and 12 months and rate of exit-site infections (ESIs) at 90 days. One hundred and seventy-seven patients initiated PD during the period of interest, and 169, 157, and 144 remained on PD at 2, 6, and 12 months, respectively. Ninety-three patients had buried catheters, and 76 patients had unburied catheters. Both groups had similar frequency of perfect appearance of exit sites at 2, 6, and 12 months (37/93 vs 41/76 at 2 months; 54/87 vs 43/70 at 6 months; 50/ 81 vs 35/ 63 at 12 months in buried and unburied groups, respectively). More patients with buried catheters had ESIs in the first 3 months (7/93 vs 1/76, p = 0.059). We conclude that exit sites of buried PD catheters do not differ qualitatively from those of unburied catheters. The trend towards more ESIs with buried catheters suggests that there may be clinical consequences of the tissue trauma at time of exteriorization.

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)0.000
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 score0.008
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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