A Standardized Diagnostic Ontology for Fibrotic Interstitial Lung Disease. An International Working Group Perspective
Bibliographic record
Abstract
Accurately diagnosing fibrotic interstitial lung disease (ILD) is a challenge even for expert clinicians. It requires multidisciplinary integration of clinical, radiological, and pathological features that are then compared against a series of formal and informal diagnostic criteria for different conditions (1). Diagnostic criteria for idiopathic pulmonary fibrosis (IPF) (2) and the remaining idiopathic interstitial pneumonias (1, 3) have helped standardize this process, but many conditions remain loosely and inconsistently defined (4–6). The current approach therefore results in significant diagnostic heterogeneity (5), which has major implications for patients whose treatment plan and prognosis depend on an accurate diagnosis. Two distinct approaches to the classification of fibrotic ILD have evolved in clinical practice. In the first approach, assignment of a diagnosis is based on strict adherence to diagnostic criteria, resulting in a large number of unclassifiable cases. In the second, assignment of a diagnosis is based on clinical judgment (i.e., what the provider believes is the likely diagnosis regardless of whether all diagnostic guideline criteria are met), generally resulting in a smaller number of unclassifiable cases. Both approaches are defensible: one maximizes diagnostic certainty at the expense of clinical utility, and the other maximizes clinical utility at the expense of diagnostic certainty (7). The lack of consistency in diagnostic approach is problematic, and we suspect is a major reason for the observed diagnostic discordance among expert centers (5).
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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 | 0.000 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".