Clinical Predictors of a Diagnosis of Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
RATIONALE: Idiopathic pulmonary fibrosis (IPF) and other idiopathic interstitial pneumonias (IIPs) have similar clinical and radiographic features, but their histopathology, response to therapy, and natural history differ. A surgical lung biopsy is often required to distinguish between these entities. OBJECTIVES: We sought to determine if clinical variables could predict a histopathologic diagnosis of IPF in patients without honeycomb change on high-resolution computed tomography (HRCT). METHODS: Data from 97 patients with biopsy-proven IPF and 38 patients with other IIPs were examined. Logistic regression models were built to identify the clinical variables that predict histopathologic diagnosis of IPF. MEASUREMENTS AND MAIN RESULTS: Increasing age and average total HRCT interstitial score on HRCT scan of the chest may predict a biopsy confirmation of IPF. Sex, pulmonary function, presence of desaturation, or distance walked during a 6-minute walk test did not help discriminate pulmonary fibrosis from other IIPs. CONCLUSIONS: Clinical data may be used to predict a diagnosis of IPF over other IIPs. Validation of these data with a prospective study is needed.
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".