Interstitial lung disease: time to rethink the snapshot diagnosis?
Bibliographic record
Abstract
An accurate and early diagnosis of idiopathic pulmonary fibrosis (IPF) is critically required for patients and care providers because it dictates very specific management decisions that include referral to transplant, access to new approved drugs, avoidance of immunosuppression and potential referral to palliative care.1 While the original diagnosis of IPF was highly dependent on patterns observed on histology, in 2002 the American Thoracic Society (ATS)/European Respiratory Society (ERS) guideline altered the approach to diagnosis so as to include a clinical–radiological–pathological multidisciplinary diagnosis.2 A careful history, searching for subtle evidence of connective tissue disease, exposures and other known causes for interstitial lung disease (ILD), was emphasised. A radiographic pattern of usual interstitial pneumonia (UIP) on high-resolution CT (HRCT) was described, characterised by basal-predominant fibrosis with peripheral reticular markings, traction airway change, architectural distortion and honeycombing. With this new classification system, it was proposed, characteristic HRCT findings could lead to a confident diagnosis of IPF without the need for a biopsy. The advantage of using HRCT patterns as a surrogate for pathological findings was particularly appealing given data describing increased risk for acute exacerbation and death following lung biopsy.3 Support for such an approach was increased by studies demonstrating agreement between radiographic and pathological findings. Raghu et al 4 found that the specificity of HRCT for UIP was 90%. Flaherty et al 5 found that the HRCT interpretation of definite UIP in biopsy-proven UIP and non-specific interstitial pneumonia (NSIP) had 100% specificity. Hunninghake et al 6 found that in a blinded prospective evaluation of patients with surgical lung biopsies, a confident HRCT diagnosis of UIP was 95% specific for the pathological finding of UIP. HRCT appearance was also predictive of survival: a definite UIP pattern on HRCT (having all the features described above) was associated with a lower survival …
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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; both teacher heads agree on what is shown here.
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".