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Record W2996914265 · doi:10.1186/s13223-019-0400-z

Kimura’s disease affecting multiple body parts in a 57-year-old female patient: a case report

2019· article· en· W2996914265 on OpenAlexvenueno aff
Bo Yu, Guoxing Xu, Xiaofan Liu, Wen Yin, Hao Chen, Baoqing Sun

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2019
Typearticle
Languageen
FieldMedicine
TopicVascular Tumors and Angiosarcomas
Canadian institutionsnot available
Fundersnot available
KeywordsDiseaseMedicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Kimura's disease (KD) is a rare chronic inflammatory disease with unknown etiology. It usually manifests as a painless soft tissue mass or subcutaneous nodule on one side of the patient's head and/or neck and rarely affects multiple parts of the body. The disease is more common among young Asian males. CASE PRESENTATION: A 57-year-old Chinese woman complained of multiple masses on her body surface. Ultrasonography was used to examine the retroperitoneal, bilateral neck, bilateral supraclavicular, bilateral axillary, and bilateral inguinal superficial lymph nodes. Enlargement of multiple lymph nodes was found in all areas. Many solid nodules were also found in the right parotid gland and right posterior neck area, respectively. Numerous solid nodules were seen on the left chest wall. Laboratory tests showed that the percentage of eosinophils in the whole blood was 39.40%, total immunoglobulin E (IgE) level was > 5000 kU/L, and serum special IgE to Phadiatop (inhaled allergens) and fx5 (food allergens) were 1.01 and 1.04 kUA/L, respectively. After a complete examination, the masses located in the right neck, retroauricular and left axillary regions, and left chest wall were resected directly. Postoperative pathological findings revealed KD. CONCLUSIONS: The case discussed in this study is extremely rare and did not meet the common affected areas and age characteristics of KD. This presentation can be used to improve disease awareness among physicians.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.097
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.293
Teacher spread0.277 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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