Kimura’s disease affecting multiple body parts in a 57-year-old female patient: a case report
Bibliographic record
Abstract
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.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".