Secondary Peritonitis in Peritoneal Dialysis: A Case Report and Review of Literature
Bibliographic record
Abstract
A 41-year-old female with Down’s syndrome on peritoneal dialysis (PD) presented with PD-related peritonitis which was not responding to guideline-directed antimicrobial therapy. Computed tomography scan revealed air in the peritoneal cavity initially suspected to be secondary to her PD. Multiple enteric bacteria were identified in the PD fluid which raised suspicion for perforation. A perforated diverticulum was eventually diagnosed with exploratory laparotomy. Spontaneous perforated viscus in patients undergoing PD is rare, but without prompt and aggressive intervention may be associated with significant morbidity and mortality. We discuss the case and review the literature highlighting the delay in the diagnoses of such cases and the role of imaging and exploratory laparotomy. Finally, recovery of multiple enteric pathogens in the workup of PD-associated peritonitis should raise the suspicion of possible viscus perforation. J Med Cases. 2018;9(9):289-292 doi: https://doi.org/10.14740/jmc3116w
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".