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Record W3140760834 · doi:10.4021/ijcp115w

Clinical Features of Acute Focal Bacterial Nephritis in Children

2013· article· en· W3140760834 on OpenAlexvenueno aff
Saito

Bibliographic record

VenueInternational Journal of Clinical Pediatrics · 2013
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAbdominal painVomitingUnconsciousnessAbscessFever of unknown originFlank painPediatricsAbdominal ultrasonographyAbdominal massComputed tomographySurgeryAnesthesia

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.389
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2013
Admission routes1
Has abstractyes

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