Short‐ and long‐term outcomes of acute diverticulitis in patients with transplanted kidneys
Bibliographic record
Abstract
AIM: The safety of nonoperative treatment for patients with transplanted kidneys who develop acute diverticulitis is unclear. Our primary aim was to examine the long-term sequelae of nonoperative management in this group. METHOD: We performed a population-based retrospective cohort study using linked administrative databases housed at ICES in Ontario, Canada. We included adult (≥18 years) patients admitted with acute diverticulitis between April 2002 and December 2019. Patients with a functioning kidney transplant were compared with those without a transplant. The primary outcome was failure of conservative management (operation, drainage procedure or death due to acute diverticulitis) beyond 30 days. The cumulative incidence function and a Fine-Grey subdistribution hazard model were used to evaluate this outcome accounting for competing risks. RESULTS: We examined 165 patients with transplanted kidneys and 74 095 without. Patients with transplanted kidneys were managed conservatively 81% of the time at the index event versus 86% in nontransplant patients. Short-term outcomes were comparable, but cumulative failure of conservative management at 5 years occurred in 5.6% (95% CI 2.3%-11.1%) of patients with transplanted kidneys versus 2.1% (95% CI 2.0%-2.3%) in those without. Readmission for acute diverticulitis was also higher in transplanted patients at 5 years at 16.7% (95% CI 10.1%-24.7%) versus 11.6% (95% CI 11.3%-11.9%). Adjusted analyses showed increased failure of conservative management [subdistribution hazard ratio (sHR) 3.24, 95% CI 1.69-6.22] and readmissions (sHR 1.55, 95% CI 1.02-2.36) for patients with transplanted kidneys. CONCLUSION: Most patients with transplanted kidneys are managed conservatively for acute diverticulitis. Although long-term readmission and failure of conservative management is higher for this group than the nontransplant population, serious outcomes are infrequent, substantiating the safety of this approach.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".