It's better to be lucky … successful management of an acute endobronchial tumour embolism in the <scp>ICU</scp>: a case report and review of the literature
Bibliographic record
Abstract
Endobronchial tumour embolism is a rare cause of acute central airway obstruction. It is primarily reported during pneumonectomy, and the outcome is frequently fatal. Successful management requires the urgent removal of tumour with rigid or flexible bronchoscopy. We present the case of a 62-year-old woman with poorly differentiated non-small cell lung cancer (NSCLC), referred to our institution for Nd:YAG laser photoresection of endobronchial tumour completely obstructing the right mainstem bronchus (RMSB). Soon after admission, our patient developed critical hypoxemia, rapidly followed by cardiac arrest. Bronchoscopy was urgently performed and revealed a necrotic tumour occluding the left mainstem bronchus (LMSB), with some residual tumour and clot at the RMSB. The tumour acutely obstructing the LMSB was successfully extracted using a foreign body retrieval basket and large flexible biopsy forceps via a large (therapeutic) flexible bronchoscope. Ventilation immediately improved, with the return of a pulse, and the patient was successfully extubated the next day. Pathology of the tumour embolism revealed NSCLC with necrosis and an adherent clot. Here, we review 16 published reports of endobronchial tumour embolism in relation to our case.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".