The High Resolution Computed Tomography in Assessment of Patients with Emphysema Following Smoking Cessation
Bibliographic record
Abstract
The purpose of our study was to evaluate the short-term effect of changes in smoking cessation on subjects with chest high resolution computed tomography (HRCT)-diagnosed emphysema, both crosssectionally and longitudinally. All patients participated in 3 months smoking cessation program.A detailed clinical history was taken and physical examination performed. We performed serum study, lung function testing and HRCT scanning to assess emphysema. After participation in the program, there was a significant increment in body mass index (0.88 kg/m2, p ＜ 0.001). There was a significant decline in forced expiratory volume in one second (3.0 % (33 ml), p ＜ 0.001), but smaller than decline in smokers. There was also a significant decline in C-reactive protein (0.40 mg/L, p ＜ 0.001) & St. George’s Respiratory Questionnaire (21, p ＜ 0.001). In CT image, there were significant decreases in mean lung density and the attenuation value separating the least 15% pixels (7.7 HU, p ＜ 0.001), but a significant increase in the percentage of the relative area of the lungs with attenuation values ＜ -950 Hounsfield unit (1.9%, p ＜ 0.001). There were significant declines in smoking, modified Medical Research Council scale, Age-Dyspnea-Obstruction (ADO) index, Dyspnea-Obstruction-Smoking-Exacerbation (DOSE) index (all p ＜ 0.001), and exacerbation (p ＜ 0.01), but a significant increase in emphysema severity (p ＜ 0.05). This study shows the possible important change of HRCT in patients with emphysema following smoking cessation.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".