A Case of Severe Fever With Thrombocytopenia Syndrome Accompanied by Self-Limiting Severe Proteinuria That Inversely Correlated With the Platelet Count
Bibliographic record
Abstract
We herein present a case of a 68-year-old man who was referred to our hospital for a tick bite accompanied by fever and general malaise. He exhibited typical clinical symptoms such as abdominal pain, diarrhea, and vomiting during hospitalization. A laboratory examination showed progressive thrombocytopenia and leukopenia during the early part of hospitalization. A definite diagnosis of severe fever with thrombocytopenia syndrome (SFTS) was made by a reverse transcription polymerase chain reaction (RT-PCR) from his blood sample in the Tokushima Prefectural Public Health Institute. Proteinuria is generally detected in more than half of patients with SFTS. In the present case, the clinical course showed a clear inverse correlation between self-limiting severe proteinuria and the platelet count during hospitalization. Although it is not currently clear how immunological interactions among the virus, platelets, and immunoglobulins affect the glomerulus or cause proteinuria, our results may contribute to elucidating the pathogenic mechanism of SFTS-associated glomerulonephritis. To the best of our knowledge, this case report is the first to show an inverse correlation between proteinuria and the platelet count in a patient with SFTS. J Med Cases. 2016;7(10):435-440 doi: http://dx.doi.org/10.14740/jmc2642w
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".