MétaCan
Menu
Back to cohort
Record W2158633689 · doi:10.1177/089686080502500412

Patient Characteristics Associated with Defects of the Peritoneal Cavity Boundary

2005· article· en· W2158633689 on OpenAlexaboutno aff
Claudia M.A. Van Dijk, Steven G. Ledesma, Isaac Teitelbaum

Bibliographic record

VenuePeritoneal Dialysis International · 2005
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePeritoneal dialysisSurgeryComplicationContinuous ambulatory peritoneal dialysisAmbulatoryHernia

Abstract

fetched live from OpenAlex

BACKGROUND: Conflicting literature exist regarding the patient characteristics that may confer an increased risk for anatomic complications of the peritoneal cavity boundaries. METHODS: We collected data from 75 randomly selected units in the United States and Canada, representing a total of 1864 peritoneal dialysis (PD) patients. RESULTS: 200 of these patients experienced a total of 217 anatomic complications between July 2000 and June 2001; 16 patients had more than 1 complication. Hernias comprised 60.4% of all complications: 24.9% inguinal, 18.9% umbilical, 13.8% ventral, 2.3% femoral, and 0.5% intrathoracic. Other complications included pericatheter or subcutaneous leak (25.3%), hydrothorax (6.0%), and miscellaneous (8.3%). Peritoneal dialysis modalities in use at the time of complication were automated PD (52.3%), continuous ambulatory PD (38.6%), and nocturnal intermittent PD (9.1%). The overall incidence of hernias was 7%. CONCLUSIONS: Logistic regression analysis found no association between hernias and age, body surface area, PD modality, volume of dialysate, time of largest dwell (day/upright vs night/recumbent), or type of catheter used. Cystic disease conferred a 2.5-fold increase in risk for anatomic complications (p < 0.001); female gender conferred an 80% reduction in risk (p < 0.0001), and Kt/V > or = 2.0 conferred a 52% reduction in risk (p < 0.05) for hernia.

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 score0.153
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations69
Published2005
Admission routes1
Has abstractyes

Explore more

Same venuePeritoneal Dialysis InternationalSame topicDialysis and Renal Disease ManagementFrench-language works237,207