Case File: Rapid Diagnosis of Pericardial Effusion
Bibliographic record
Abstract
Mr. DB was a 95 year old man who presented to the emergency department with dyspnea progressing over the last 3 months. Chest x-ray demonstrated an enlarged cardiac silhouette. He had a past medical history significant for coronary artery disease, hypertension and a lobectomy due to tuberculosis. A point of care cardiac ultrasound was conducted by an internal medicine resident as part of his physical examination in the emergency department. A large pericardial effusion was found. There were no clinical signs of tamponade. Video 1 (online supplement; Figure 1) demonstrates a parasternal long axis view with the pericardial effusion noted to be posterior to the left ventricle in this view. Video 2 (online supplement; Figure 2) is a short axis view of the heart which is showing that the effusion is surrounding the heart. Video 3 and 4 (online supplements; Figures 3 & 4) demonstrates that the pericardial effusion is present significantly surrounding the apex as well. An echocardiogram confirmed the POCUS findings and cardiology was consulted to conduct a pericardiocentesis, following which the patient’s symptoms resolved. The effusion was thought to be chronic and transudative. In this case, the use of POCUS at the bedside allowed for rapid detection of a large pericardial effusion and subsequent treatment.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.135 | 0.018 |
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".