Multiloculated Liver Abscess Due to Hypermucoviscous Klebsiella pneumoniae
Bibliographic record
Abstract
(See page 257 for the Photo Quiz.) Diagnosis: Klebsiella pneumoniae liver abscess with sepsis and bacteremia. The primary infectious differential diagnosis of ring-enhancing liver lesions includes echinococcal, amoebic, and pyogenic liver abscess. However, Echinococcus granulosis is not endemic in the Philippines [1], and radiographic features considered pathognomonic for hydatid cysts (such as the presence of a laminar layer or of daughter cysts that contain fluid of lower density than the surrounding mother cyst fluid [2]) were absent. In this case, epidemiologic, historic, and imaging characteristics (see Figure 1) pointed toward a diagnosis of pyogenic liver abscess, while growth of bacterial colonies with features characteristic of K. pneumoniae on blood agar (see Figure 2) confirmed the diagnosis. In recent series, pyogenic liver abscess has been increasingly common in patients of Asian descent [3, 4]. A previous history of biliary disease, as in this case, is an important risk factor for pyogenic liver abscess [4]. Further, the most common radiographic finding in amoebic liver abscess (ALA) is a single, nonloculated subcapsular abscess (85% in one series); the presence of multiple abscesses argues against a diagnosis of ALA [5, 6]. Multiloculated liver abscesses are not uncommon in case series’ of K. pneumoniae liver abscess, although the more common finding is that of a single, right-sided lesion [3].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".