A Case Series of Two Patients Presenting With Pericardial Effusion as First Manifestation of Non-Small Cell Lung Cancer With <i>BRAF</i> Mutation and Expression Of PD-L1
Bibliographic record
Abstract
Lung cancer is the number one cause of cancer-related deaths in the United States. Involvement of pericardium occurs once cancer has progressed to stage IV which can cause massive effusion in the pericardial sac. This can lead to cardiac tamponade which can be fatal very quickly if untreated. The following is a two patient case series in which both patients presented with large pericardial effusion. The first patient sought medical attention due to new onset palpitations and was found to have hemorrhagic pericardial effusion and pulmonary embolism (PE). The second patient presented with shortness of breath. Investigations revealed that she had pericardial and pleural effusions along with multiple metastases throughout the body. Both patients ended up with a diagnosis of non-small cell lung cancer (NSCLC) with BRAF mutation. One patient had V600E mutation; other patient had a variant p.D594N mutation. Both patients also had expression of PD-L1. World J Oncol. 2018;9(2):56-61 doi: https://doi.org/10.14740/wjon1092w
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 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".