Clinical Features of Acute Focal Bacterial Nephritis in Children
Bibliographic record
Abstract
Background: Acute focal bacterial nephritis (AFBN) is a localized bacterial infection of the kidneys. Patients present with an inflammatory mass without frank abscess formation, which may represent a relatively early stage of renal abscess. In children, most patients with AFBN present with non-specific findings of fever and flank or abdominal pain. Methods: From 2008 to 2011, AFBN was diagnosed in 11 children at the Department of General Pediatrics, Nihon University Nerima Hikarigaoka Hospital, Tokyo, Japan. Clinical data of 11 cases (four girls and seven boys) with a mean age of 5.4 years (range 2- 8 years) were available for retrospective evaluation. Results: All children presented with fever and rapid deterioration of their clinical condition. Six children suffered from gastrointestinal symptoms such as vomiting and abdominal pain. Four children suffered from neurological symptoms, including meningeal irritation, unconsciousness, and seizure. In renal ultrasonography, abdominal findings were seen in four patients. However, abdominal enhanced computed tomography (CT) was indispensible for diagnosis of AFBN in these patients. Conclusions: We recommend that abdominal enhanced CT should be performed for patients with fever of unknown origin. AFBN should be suspected in children with fever and rapid deterioration of clinical condition. Int J Clin Pediatr. 2013;2(2):68-73 doi: http://dx.doi.org/10.4021/ijcp115w
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".