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Record W4408149409 · doi:10.1159/000544053

A Case of Post-Endobronchial Ultrasound Polymicrobial Pericarditis and Mediastinitis in Metastatic Renal Cell Carcinoma

2025· article· en· W4408149409 on OpenAlexaff
Viktor Sekowski, Waël C. Hanna

Bibliographic record

VenueCase Reports in Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicPericarditis and Cardiac Tamponade
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMediastinitisRenal cell carcinomaPericarditisRadiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) is a frequently used bronchoscopic method for sampling centrally located tumors and accessible mediastinal lymph nodes. It is widely employed for staging and obtaining tissue diagnosis in lung cancer, but it can also be used for other conditions with mediastinal lymph node metastases. Although the reported complication rate for EBUS-TBNA varies, it consistently remains low, including for severe infectious complications such as pericarditis and mediastinitis. Case Presentation: We present a unique case of polymicrobial pyogenic pericarditis, mediastinitis, and death in a patient who underwent EBUS-TBNA for the evaluation of solitary mediastinal lymphadenopathy in the background of previously resected renal cell carcinoma. There were no complications during her procedure and her mediastinal lymph node biopsy confirmed metastatic renal cell carcinoma. However, 23 days after her procedure, she presented to the emergency department with sepsis and cardiac tamponade. Pericardial fluid cultures and computed tomography imaging confirmed polymicrobial pyogenic pericarditis and mediastinitis. Conclusion: While complications from EBUS-TBNA are rare, clinicians need to be aware that pericarditis and mediastinitis are possible life-threatening complications in order to facilitate early recognition and interventions.

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)0.001
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.012
GPT teacher head0.296
Teacher spread0.284 · 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 designCase report
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

Citations0
Published2025
Admission routes1
Has abstractyes

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