Klebsiella pneumonia-induced prostate abscess: How to work it up?
Bibliographic record
Abstract
INTRODUCTON: Klebsiella pneumonia (KP) is related to a metastatic phenomenon from the originally affected primary organ. About 28% of patients with pyogenic liver abscess arising from KP suffer from metastatic complications. This study was done to define the clinical features of KP-induced prostate abscess. METHODS: A total of 14 patients were diagnosed with prostate abscess based on clinical, laboratory examination and abdominopelvic computed tomography (CT) scan from 2007 to 2013. RESULTS: Among these 14 patients, KP was the dominant causative microorganism in 6 patients (42.9%), followed by Esherchia coli in 2, Pseudomonas aeroginosa in 1, methicillin-resistant Staphyolcoccus aureus in 1, and no growth in either the urine or blood culture in 4. Four (66.7%) of the 6 KP induced-prostate abscess had other concurrent abscess sites besides the prostate: liver in 3, kidney in 1, and perianal area with endogenous endophthalmitis that ended in loss of vision in 1 patient. CONCLUSIONS: We report on the clinical features of KP-induced prostate abscess based on a small number of patients, which is the main limitation of our study. We believe that if the causative organism of a prostate abscess was KP, more workup would be needed to rule out the presence of an abscess in other organs, especially in the liver. Abdominopelvic CT scan would be a proper imaging modality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".