Bibliographic record
Abstract
Yersinia pseudotuberculosis is a gram-negative bacillus from the family of Yersiniacae. Similar to the other member of the family, Yersinia enterocolitica, Y. pseudotuberculosis most commonly presents with enteric illness [1]. The sources of infection with Y. pseudotuberculosis include contaminated food such as dairy and vegetables or contaminated water [2]. Direct contact with animals including dogs, rodents, rabbits, farm animals or birds has also been described as other modes of acquisition [1,2]. Community outbreaks secondary to contaminated lettuce, carrots, and water have been reported in Finland, Japan, Russia and Canada [3,4]. Although it often presents as mild gastroenteritis, several other clinical manifestations have been reported with Y. pseudotuberculosis [1]. Pseudoappendicitis with mesenteric lymphadenitis is a well described phenomenon and many patients in the 20th century underwent appendectomies due to erroneous diagnosis after presenting with right lower quadrant abdominal pain [5]. Bacteremia is rare and when it occurs it has been associated with abscesses in spleen, liver, kidneys, lung with granulomatous appearance that mimics tuberculosis [5,6]. Although it is commonly a self-limiting illness in immunocompetent patients without liver disease, septicemia with Y. pseudotuberculosis is thought to carry a fatality rate that exceeds 75% without antibiotics [2]. Here we present a case of Yersinia pseudotuberculosis bacteremia with splenic abscesses in a patient with previously undiagnosed liver cirrhosis.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.801 | 0.579 |
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".