Diagnostic Likelihood Thresholds That Define a Working Diagnosis of Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
Abstract Rationale The level of diagnostic likelihood at which physicians prescribe antifibrotic therapy without requesting surgical lung biopsy (SLB) in patients suspected of idiopathic pulmonary fibrosis (IPF) is unknown. Objectives To determine how often physicians advocate SLB in patient subgroups defined by IPF likelihood and risk associated with SLB, and to identify the level of diagnostic likelihood at which physicians prescribe antifibrotic therapy with requesting SLB. Methods An international cohort of respiratory physicians evaluated 60 cases of interstitial lung disease, giving:1) differential diagnoses with diagnostic likelihood;2) a decision on the need for SLB; and3) initial management. Diagnoses were stratified according to diagnostic likelihood bands described by Ryerson and colleagues. Measurements and Main Results A total of 404 physicians evaluated the 60 cases (24,240 physician–patient evaluations). IPF was part of the differential diagnosis in 9,958/24,240 (41.1%) of all physician–patient evaluations. SLB was requested in 8.1%, 29.6%, and 48.4% of definite, provisional high-confidence and provisional low-confidence diagnoses of IPF, respectively. In 63.0% of provisional high-confidence IPF diagnoses, antifibrotic therapy was prescribed without requesting SLB. No significant mortality difference was observed between cases given a definite diagnosis of IPF (90–100% diagnostic likelihood) and cases given a provisional high-confidence IPF diagnosis (hazard ratio, 0.97;P = 0.65; 95% confidence interval, 0.90–1.04). Conclusions Most respiratory physicians prescribe antifibrotic therapy without requesting an SLB if a provisional high-confidence diagnosis or “working diagnosis” of IPF can be made (likelihood ≥ 70%). SLB is recommended in only a minority of patients with suspected, but not definite, IPF.
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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 | 0.013 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".