Patient Characteristics Associated with Defects of the Peritoneal Cavity Boundary
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".