Diagnostic challenges and good treatment outcomes in pediatric paraganglioma of the abdomen
Bibliographic record
Abstract
RATIONALE: Paraganglioma is a catecholamine-producing neuroendocrine tumor. Management of paraganglioma including its diagnosis is difficult, because it has no characteristic symptoms and many diseases can manifest as headache and high blood pressure. Herein, we report a rare case of paraganglioma of the abdomen with headache and initial normal blood pressure. PATIENT CONCERNS: A 9-year-old Chinese girl was hospitalized because of intermittent headache persisting for more than 9 months and recurrent headache for 15 days, accompanied by weight loss, impaired heat tolerance, and otherwise normal blood pressure. DIAGNOSES: We eventually diagnosed paraganglioma. INTERVENTIONS: Her paroxysmal hypertension subsided over 1 month after surgical removal of the tumor. LESSONS: Intermittent headache and normal hypertension as the initial symptoms of paraganglioma can easily lead to misdiagnosis as another disease (e.g., renal artery stenosis, primary hyperaldosteronism, Takayasu's arteritis), and its differential diagnosis is difficult. When a patient presents with intermittent hypertension, clinicians should consider a diagnosis of paraganglioma. The comprehensive use of ultrasonography, computed tomography (including enhanced computed tomography and 3D reconstruction), magnetic resonance imaging, and plasma catecholamine measurement can aid the diagnosis of paraganglioma.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".