The Relationship between Body Mass Index and Organism-Specific Peritonitis
Bibliographic record
Abstract
Background Obesity is increasingly prevalent worldwide, and a greater number of patients initiate renal replacement therapy with a high body mass index (BMI). This study aimed to evaluate the association between BMI and organism-specific peritonitis. Methods All adult patients who initiated peritoneal dialysis (PD) in Australia between January 2004 and December 2013 were included. Data were accessed through the Australia and New Zealand Dialysis and Transplant (ANZDATA) Registry. The co-primary outcomes of this study were time to first organism-specific peritonitis episode, specifically gram-positive, gram-negative, culture-negative, and fungal. Secondary outcomes were individual rates of organism-specific peritonitis for the same 4 microbiological categories. Results There were 7,381 peritonitis episodes among the 8,343 incident PD patients evaluated. After multivariable adjustment, obese patients (BMI 30 – 34.9 kg/m 2 ) had an increased risk of fungal peritonitis (adjusted hazard ratio [HR] 1.69, 95% confidence interval [CI] 1.18 – 2.42), very obese patients (BMI ≥ 35 kg/m 2 ) had a significantly higher risk of gram-positive peritonitis (HR 1.15, 95% CI 1.02 – 1.30), while both obese and very obese patients experienced significantly higher risks of gram-negative peritonitis (HR 1.29, 95% CI 1.11 – 1.50 and HR 1.30, 95% CI 1.08 – 1.57, respectively) compared with patients with normal BMI (20 – 24.9 kg/m 2 ). Obesity and severe obesity were independently associated with increased incidence rate ratios of all forms of organism-specific peritonitis with a non-significant trend for severe obesity and gram-negative peritonitis association. Conclusion Among Australian patients, obesity and severe obesity are associated with significantly increased rates of gram-positive, gram-negative, fungal, and culture-negative peritonitis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".