Progressive damage on high resolution computed tomography despite stable lung function in cystic fibrosis
Bibliographic record
Abstract
For effective clinical management of cystic fibrosis (CF) lung disease it is important to closely monitor the start and progression of lung damage. The aim of this study was to investigate the ability of high-resolution computed tomography (HRCT) scoring systems and pulmonary function tests (PFT) to detect changes in lung disease. CF children (n=48) had two HRCT scans in combination with two PFT 2 yrs apart. Their scans were scored using five scoring systems (Castile, Brody, Helbich, Santamaria and Bhalla). "Sensitivity" was defined as the ability to detect disease progression. In this group of children, HRCT scores worsened. PFT remained unchanged or improved. Of the HRCT parameters, mucous plugging and the severity, extent and peripheral extension of bronchiectasis worsened significantly. Relationships between changes in HRCT scores and PFT were weak. Substantial structural lung damage was evident in some children who had normal lung function. These data show that high-resolution computed tomography is more sensitive than pulmonary function tests in the detection of early and progressive lung disease, and suggest that high-resolution computed tomography may be useful in the follow up of cystic fibrosis children and as an outcome measure in studies that aim to reduce lung damage.
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 | 0.005 |
| 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 | 0.001 |
| 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".