Conventional High-Resolution CT Versus Helical High-Resolution MDCT in the Detection of Bronchiectasis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to compare conventional high-resolution CT (HRCT) with helical 16-MDCT in the detection of bronchiectasis. MATERIALS AND METHODS: We retrospectively evaluated 80 patients including 61 with bronchiectasis (mean age, 64 years; range, 22-87 years) and a control group of 19 patients with normal MDCT of the chest. Two sets of images were blindly, randomly analyzed by two observers: contiguous 1-mm slices (MDCT set) and 1-mm slices every 10 mm (HRCT set) derived from the MDCT set. Images were scored independently for presence, extent, and severity of bronchiectasis, followed by a consensus interpretation. Kappa analysis assessed inter- and intraobserver agreement. MDCT was the radiologic gold standard. RESULTS: Of the 61 patients with bronchiectasis diagnosed with MDCT, 56 (92%) were positive for bronchiectasis on conventional HRCT. Seven patients had positive MDCT scans only, and two patients had positive HRCT scans only. Of 479 lobes, 59 were positive for bronchiectasis on MDCT and negative on HRCT, and 19 lobes were positive for bronchiectasis on HRCT and negative on MDCT (p < 0.0001). MDCT showed 25 more lobes with cylindric, 11 more lobes with varicose, and four more lobes with cystic bronchiectasis than did HRCT. Sensitivity, specificity, and positive and negative predictive values of HRCT in detecting bronchiectasis were 71%, 93%, 88%, and 81%, respectively. Interobserver agreement for presence, extent, and severity of bronchiectasis ranged from moderate to good for MDCT (kappa values, 0.64, 0.5, and 0.48, respectively) and poor to good for HRCT (kappa values, 0.65, 0.46, and 0.25, respectively). CONCLUSION: Contiguous helical 16-MDCT with 1-mm collimation is superior to HRCT at 10-mm intervals in showing the presence and extent of bronchiectasis.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".