Histopathologic Assessment of Suspected Idiopathic Pulmonary Fibrosis: Where We Are and Where We Need to Go
Bibliographic record
Abstract
CONTEXT.—: Accurate diagnosis of idiopathic pulmonary fibrosis (IPF) requires multidisciplinary diagnosis that includes clinical, radiologic, and often pathologic assessment. In 2018, the American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and the Latin American Thoracic Society (ATS/ERS/JRS/ALAT) and the Fleischner Society each published guidelines for the diagnosis of IPF, which include criteria for 4 categories of confidence of a histologic usual interstitial pneumonia (UIP) pattern. OBJECTIVE.—: To (1) identify the role of the guidelines in pathologic assessment of UIP; (2) analyze the 4 guideline categories, including potential areas of difficulty; and (3) determine steps the Pulmonary Pathology Society and the greater pulmonary pathology community can take to improve current guideline criteria and histopathologic diagnosis of interstitial lung disease. DATA SOURCES.—: Data were derived from the guidelines, published literature, and clinical experience. CONCLUSIONS.—: Both guidelines provide pathologists with a tool to relay to the clinician the likelihood that a biopsy represents UIP, and serve as an adjunct, not a replacement, for traditional histologic diagnosis. There are multiple challenges with implementing the guidelines, including (1) lack of clarity on the quantity and quality of histologic findings required, (2) lack of recognition that histologic features cannot be assessed independently, and (3) lack of guidance on how pathologists should incorporate clinical and radiographic information. Current criteria for "probable UIP" and "indeterminate for UIP" hinder accurate reflection of the likelihood of IPF. These challenges highlight the need for further morphologic-based investigations in the field of pulmonary pathology.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".