Hemoptysis: Comparison of Diagnostic Accuracy of Multi Detector CT Scan and Bronchoscopy
Bibliographic record
Abstract
BACKGROUND: Hemoptysis is the expectorating of blood from the tracheobronchial tree or pulmonary parenchyma. There is conflicting information about usefulness of radiography, MDCT, and bronchoscopy for investigating site and cause of the bleeding in patients with hemoptysis. The present study attempted to evaluated efficacy of these methods for identifying hemoptysis' cause and etiology on 40 patients with the disease. METHODS: A total of 40 patients with Hemoptysis who were referred to Golestan and Emam Khomeini hospitals were evaluated. Complete history of symptoms, volume and duration of Hemoptysis and demographic information were documented. Radiography, MDCT, and bronchoscopy were performed on all patients in order to investigate the site and cause of the bleeding. RESULTS: Results showed MDCT had higher efficacy in identifying bleeding site than radiography, while efficacy of radiography and bronchoscopy or efficacy of MDCT and bronchoscopy weren't significantly different. In addition, sensitivity of MDCT (60%) for detecting cause of the bleeding was higher than that of radiography (25%) and bronchoscopy (32.5%). CONCLUSION: The present study suggests MDCT as a suitable method in screening patients with hemoptysis, because it managed to detect site and causes of bleeding more efficiently than other methods. Additionally, we concluded that MDCT is an appropriate technique for diagnosing malignancies that cause hemoptysis in patients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".