Is HRCT the best way to diagnose idiopathic interstitial fibrosis?
Bibliographic record
Abstract
PURPOSE OF REVIEW: High-resolution computed tomography (HRCT) has been the major advance in the diagnosis of idiopathic interstitial pneumonias in the last two decades. In diffuse lung diseases, HRCT now has a central role in routine diagnostic evaluation, and a major impact on the utility of other diagnostic tests, especially bronchoalveolar lavage and surgical lung biopsy. RECENT FINDINGS: Numerous published studies have evaluated the accuracy of HRCT. The clinical information was not always utilized to generate a noninvasive diagnosis, however. Despite failure to identify idiopathic pulmonary fibrosis on HRCT in a significant minority of cases, given compatible clinical data, characteristic HRCT appearances justify noninvasive diagnosis in most patients. The limitations of the published studies highlight importance of integrating HRCT data with baseline clinical information and, in selected cases, histopathologic findings. SUMMARY: When HRCT and clinical findings are both typical of an individual diffuse lung disease, i.e. 'pathognomonic', it is generally appropriate to institute management based on a confident noninvasive diagnosis. When clinical and HRCT data are divergent, or when HRCT features are 'indeterminate', however, histologic evaluation continues to play an essential role. Integration of histology with radiologic and clinical data is the best way to formulate the final diagnosis in these cases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".