Successful treatment of recurrent pleural and pericardial effusions with tocilizumab in a patient with systemic lupus erythematous
Bibliographic record
Abstract
A 22-year-old Caucasian man presented to hospital with pleuritic chest pain. He had had a history of a sun-sensitive rash a year prior. Workup revealed normal cardiac enzymes and chest X-ray. However, electrocardiogram revealed ST elevation and PR depression, and echocardiogram revealed a slight pericardial effusion without other findings. A diagnosis of pericarditis was made. Subsequently, he was found to be positive for antinuclear antibodies (ANAs), as well as antibodies to SSA, SSB and double-stranded DNA; C3 was low, and C4 was undetectable. A diagnosis of systemic lupus erythematosus was made. The patient initially responded to high-dose ibuprofen. One month later, he developed a new pericardial effusion, this time with concomitant massive left-sided pleural effusion, requiring three separate thoracenteses draining a total of 6 L of pleural fluid. The recurrent effusion failed to respond to high-dose corticosteroid treatment. Owing to the severity and rapidity of the recurrence of pleural and pericardial effusion, intravenous tocilizumab was administered. The patient had excellent clinical and radiographic improvement. This case shows that tocilizumab may have a role in the treatment of intractable pleuropericardial effusion and other forms of lupus-associated serositis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".