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Record W2807367751 · doi:10.3747/pdi.2017.00255

Peritoneal Dialysis in Adult Patients with Permanent Suprapubic Catheter: A Single-Center Experience and Literature Review

2018· article· en· W2807367751 on OpenAlexaff
Abdullah Al‐Hwiesh, Ibrahiem Saeed Abdul‐Rahman, Mohammad Ahmed Nasr El-Din, Amani Alhwiesh, Aisha M. Al-Osail, Tamer El‐Salamony, Nadia Al‐Audah, Abdalla Abdelrahman, Nehad Al‐Audah, Hani E. Mansour, Khalid Al‐Otaibi

Bibliographic record

VenuePeritoneal Dialysis International · 2018
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsPeritoneal dialysisMedicineSingle CenterCatheterSurgery

Abstract

fetched live from OpenAlex

Background The aim of this paper was to review the feasibility of peritoneal dialysis (PD) in the presence of a permanent supra-pubic catheter over a long follow-up period. Methods Twelve patients with automated PD and permanent suprapubic catheters were studied for complications over a period of 10 years. Results In all 12 patients, PD went smoothly. Two of our patients required removal of the PD catheter due to peritonitis. The overall rate of exit-site infection throughout the study was 41.7/patient-month and the difference between patients 60 years or older and those 25 years or younger was not significant ( p = 0.3673). The overall peritonitis rate for all patients was 38.3 episode/patient-month, and none of the patients with ventriculoperitoneal shunt (VPS) had peritonitis. All patients with episodes of infection responded well to the proper antibiotics. Conclusions The available data supported the feasibility of PD in patients with permanent suprapubic catheters; however, a greater number of patients with a longer follow-up period need to be studied to support our results.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.257
Teacher spread0.249 · 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 designSystematic review
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

Citations1
Published2018
Admission routes1
Has abstractyes

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