Colonoscopy in Automated Peritoneal Dialysis Patients: Value of Prophylactic Antibiotics: A Prospective Study on a Single Antibiotic
Bibliographic record
Abstract
OBJECTIVE: To evaluate the need for prophylactic antibiotics in automated peritoneal dialysis (APD) patients undergoing flexible colonoscopy. PATIENTS AND METHODS: A total of 93 patients on automated peritoneal dialysis (APD) undergoing diagnostic colonoscopy were enrolled in a prospective, randomized study. Patients were randomized into 2 age- and sex-matched groups; group A (46 patients) with intraperitoneal (IP) ceftazidime prior to colonoscopy and group B (47 patients) without prophylactic antibiotics. The relations between peritonitis and different parameters were analyzed. RESULTS: Of all colonoscopies, 60.2% showed normal findings, 17.2% with colonic polyps at different sites, 12.9% with angiodysplastic-like lesions, 5.4% with colonic ulcer(s), 3.2% with diverticulae without diverticulitis and 1.1% had transverse colon stricture. Post-colonoscopy peritonitis was documented in 3 (6.5%) and 4 (8.5%) patients in groups A and B, respectively (p = 0.2742); the causative organisms were mainly gram negative bacteria. Polypectomy was not associated with increased peritonitis episodes. By multiple logistic regression analysis, diabetes mellitus was the only independent variable that entered into the best predictive equation over the development of post-colonoscopy peritonitis but not antibiotic use. CONCLUSIONS: The relation between prophylactic antibiotic use prior to colonoscopy in APD patients and the risk of peritonitis was lacking. Only diabetes mellitus appears to be of significance. Polypectomy did not increase peritonitis episodes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".