Idiopathic Pulmonary Fibrosis: CT and Risk of Death
Bibliographic record
Abstract
PURPOSE: To investigate the prognostic value of quantitative computed tomographic (CT) scoring for the extent of fibrosis or emphysema in the context of a clinical model that includes the gender, age, and physiology ( GAP gender, age, and physiology model) of the patient. MATERIALS AND METHODS: Study cohorts were approved by local institutional review boards, and all patients provided written consent. This was a retrospective cohort study that included 348 patients (246 men, 102 women; mean age, 69 years ± 9) with idiopathic pulmonary fibrosis from two institutions. Fibrosis and emphysema visual scores were independently determined by two radiologists. Models were based on competing risks regression for death and were evaluated by using the C index and reclassification improvement. RESULTS: The CT- GAP gender, age, and physiology model (a modification of the original GAP gender, age, and physiology model that replaces diffusion capacity of carbon monoxide with CT fibrosis score) had accuracy comparable to that of the original GAP gender, age, and physiology model, with a C index of 70.3 (95% confidence interval: 66.4, 74.0); difference in C index compared with the GAP gender, age, and physiology model of -0.4 (95% confidence interval: -2.2, 3.4). The performance of the original GAP gender, age, and physiology model did not change significantly with the simple addition of fibrosis score, with a change in C index of 0.0 (95% confidence interval: -1.8, 0.5) or of emphysema score, with a change in C index of 0.0 [95% confidence interval: -1.3, 0.4]). CONCLUSION: CT fibrosis score can replace diffusion capacity of carbon monoxide test results in a modified GAP gender, age, and physiology model (the CT- GAP gender, age, and physiology model) with comparable performance. This may be a useful alternative model in situations where CT scoring is more reliable and available than diffusion capacity of carbon monoxide.
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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 | 0.006 |
| 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 | 0.000 |
| 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".