High-Resolution Computed Tomography in Assessment of Patients With Emphysema
Bibliographic record
Abstract
OBJECTIVE: To assess whether high-resolution computed tomography (HRCT) variables are as good as other known clinical variables in grading emphysema patients. METHODS: A detailed clinical history was taken and physical examination performed. We performed serum study, lung function testing, and HRCT scanning to assess emphysema. Mean lung density, the attenuation value separating the least 15% of pixels (PERC15), the percentage of the relative area of the lungs with attenuation values < -950 Hounsfield units (HU) (RA950), and histogram analysis were calculated from computerized data. RESULTS: The final analysis was based on data from 92 subjects, and they were moderately emphysematous (mean lung density was -877 ± 23 HU, PERC15 was -953 ± 21 HU, and RA950 was 16 ± 5%). There was a significant difference regarding subjective emphysema severity in the St George's Respiratory Questionnaire, smoking history, FEV1, C-reactive protein, age, and body mass index (P < .001). There was a significant correlation between the 3 objective image variables and the 6 objective clinical variables (St George's Respiratory Questionnaire, smoking history, FEV1, C-reactive protein, age, and body mass index) (P < .001). CONCLUSIONS: This study shows the possible important role of HRCT in the diagnosis and quantification of pulmonary emphysema.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".