Dialysis-Associated Peritonitis due to Serratia marcescens: A Case Report
Bibliographic record
Abstract
Introduction: Serratia marcescens (SM) is a Gram-negative, opportunistic, highly virulent bacterium and a rare pathogen that causes peritonitis Case reports, a Canadian case series, and a systematic review in Hong Kong suggested that peritonitis in PD due to SM was associated with high rates of treatment failure and catheter removal We present a clinical case of peritonitis due to SM that led to catheter loss. Case Description: A 34-year-old man with a history of end-stage renal disease secondary to diabetes on continuous ambulatory PD for 5 years with a history of 2 previous cases of peritonitis with adequate resolution of these and with peritoneal fluid (PF) cultures without microorganism development Admitted for severe abdominal pain, nausea, vomiting and diarrhea in addition to cloudy PF cytology was requested where leukocytes were “uncountable”, being 100% polymorphonuclear (PNL), empirically started intravenous ceftriaxone and vancomycin; PF culture was collected where SM was isolated, treatment was continued and cytology was taken again on the third day of treatment finding leukocytes of 1130, being 90% PNL, thinking in adequate response antibiotics were continued for 7 days, however, he presented fever, cytology reporting 3456 leukocytes with 90% of PNL and new culture with isolation of SM again, showing sensitivity to meropenem by meropenem, showing sensitivity to meropenem by meropenem, and the patient was treated with a new culture, She showed sensitivity to meropenem, so it was decided to change the antibiotic, which was effective for 3 days until she presented clinical deterioration and a referral to nephrology was requested, who decided to change to HD and remove the catheter, during the removal she presented purulent material and multiple adhesions, so it was decided that the cavity was not useful and definitive HD; She completed 14 days of treatment with meropenem, presenting improvement and it was decided to discharge her. Discussion: Peritonitis due to SM is a serious complication, so its surveillance should be intensified and appropriate treatment compatible with the antibiogram should be offered. Low socioeconomic status, poor home environment and hygienic conditions increase the rates of peritonitis due to this pathogen, so more frequent and careful patient and family education in such conditions may improve patient care.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| 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".