Systemic Air Embolism Complicating Computed Tomography–guided Percutaneous Transthoracic Biopsy of Cavitary Lung Lesions
Bibliographic record
Abstract
PURPOSE: Cavitary lung lesions often pose a diagnostic challenge, and tissue sampling can be required to obtain a confident diagnosis. Many authors contend that a computed tomography-guided percutaneous transthoracic lung biopsy (PTLB) of a cavitary lung lesion places a patient at higher risk for systemic air embolism (SAE) compared with biopsy of a noncavitary lesion. MATERIALS AND METHODS: We reviewed the literature for studies of SAE complicating PTLB. We searched English-language articles indexed through PubMed, Embase, and Ovid Medline and included articles published up to March 31, 2020. RESULTS: We identified 10 case reports of SAE complicating PTLB, and 3 case-cohort studies comparing cavitary and noncavitary lesion biopsy. Among the case-cohort studies reviewed, 4 SAE occurred among 145 biopsies of cavitary lesions (2.7%), and 65 SAE occurred among 3050 biopsies of noncavitary lesions (2.1%). The pooled odds ratio of PTLB complicating SAE of cavitary lesions compared with noncavitary lesions was 1.29 (95% confidence interval: 0.47-3.60). No deaths following SAE after computed tomography-guided PTLB of cavitary lesions were reported in recent literature. CONCLUSIONS: On the basis of available evidence, air embolism rates are similar for PTLB of cavitary and noncavitary lesions. Additional research and registry studies are necessary to better understand this topic.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".