Clinical Impact of Multidetector Row Computed Tomography before Bronchial Artery Embolization in Patients with Hemoptysis: A Prospective Study
Bibliographic record
Abstract
PURPOSE: To evaluate prospectively the role and impact of multidetector row computed tomography (MDCT) before bronchial artery embolization (BAE) in patients with hemoptysis. METHODS: MDCT of the thorax was performed in 27 patients (21 men, 6 women; age range, 22-70 years; mean, 39 years) with hemoptysis who were referred for BAE. Transverse, multiplanar reconstruction, and 3-dimensional reconstruction (maximum intensity projection and volume rendered) images were analysed to identify the abnormal hypertrophied bronchial and nonbronchial systemic arteries causing hemoptysis, their origin and course were noted. Digital subtraction angiography was performed with the knowledge of findings of MDCT. Selective arteriogram of abnormal bronchial as well as nonbronchial arteries was performed. Embolization was attempted in 25 of these patients (92.6%) by using polyvinyl alcohol particles (350-500 μm), Gelfoam or Embospheres (400-700 μm). Follow-up was done for a mean period of 20.5 months. RESULTS: Based on MDCT, 2 of 27 patients were found unsuitable for BAE. On computed tomography, 38 arteries (27 bronchial and 11 nonbronchial systemic arteries) were identified as abnormal hypertrophied vessels. On angiography, 34 of these arteries (25 bronchial and 9 nonbronchial systemic arteries) were found to be responsible for hemoptysis. Three of these arteries could not be evaluated during angiography, and 1 artery that was identified as abnormal on computed tomography was found normal on angiography. All 25 bronchial and 9 nonbronchial systemic arteries that cause hemoptysis were detected at MDCT. Embolization was successful in 23 of 25 patients. CONCLUSION: MDCT enables detection and depiction of all bronchial and nonbronchial systemic arteries causing hemoptysis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".