Integrating Clinical Probability into the Diagnostic Approach to Idiopathic Pulmonary Fibrosis: An International Working Group Perspective
Bibliographic record
Abstract
Abstract Background When considering the diagnosis of idiopathic pulmonary fibrosis (IPF), experienced clinicians integrate clinical features that help to differentiate IPF from other fibrosing interstitial lung diseases, thus generating a “pre-test” probability of IPF. The aim of this international working group perspective was to summarize these features using a tabulated approach similar to chest HRCT and histopathologic patterns reported in the international guidelines for the diagnosis of IPF, and to help formally incorporate these clinical likelihoods into diagnostic reasoning to facilitate the diagnosis of IPF. Methods The committee group identified factors that influence the clinical likelihood of a diagnosis of IPF, which was categorized as a pre-test clinical probability of IPF into “high” (70–100%), “intermediate” (30–70%), or “low” (0–30%). After integration of radiological and histopathological features, the post-test probability of diagnosis was categorized into “definite” (90–100%), “high confidence” (70–89%), “low confidence” (51–69%), or “low” (0–50%) probability of IPF. Findings A conceptual Bayesian framework was created, integrating the clinical likelihood of IPF (“pre-test probability of IPF”) with the HRCT pattern, the histopathology pattern when available, and/or the pattern of observed disease behavior, into a “post-test probability of IPF.” The diagnostic probability of IPF was expressed using an adapted diagnostic ontology for fibrotic interstitial lung diseases. Interpretation The present approach will help incorporate the clinical judgment into the diagnosis of IPF, thus facilitating the application of IPF diagnostic guidelines and, ultimately improving diagnostic confidence and reducing the need for invasive diagnostic techniques.
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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 | 0.159 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.004 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".