Interobserver agreement regarding the Fleischner Society diagnostic criteria for usual interstitial pneumonia patterns on computed tomography
Bibliographic record
Abstract
Objective: To assess interobserver agreement among radiologists regarding the current Fleischner Society diagnostic criteria for usual interstitial pneumonia (UIP) patterns on computed tomography (CT). Materials and Methods: Using the Fleischner Society criteria for UIP CT patterns, five raters, working independently, categorized the high-resolution CT (HRCT) scans of 44 patients with interstitial lung disease who underwent lung biopsy. The raters also evaluated the presence, extent, and distribution of the most relevant imaging findings, as well as indicating their level of confidence in the most likely diagnosis and in up to three diagnostic hypotheses. Results: There was moderate to substantial interobserver agreement regarding the UIP patterns on HRCT-kappa statistic (κ) = 0.59-0.61. Interobserver agreement for the binary scores was substantial (κ = 0.77-0.79), whereas that for the presence of honeycombing was almost perfect (κ = 0.81-0.96). There was agreement regarding at least one of the three diagnostic hypotheses in only 36.4% of the cases. For the level of confidence in the most likely diagnosis, there was only slight to fair agreement (κ = 0.19-0.21). Conclusion: Interobserver agreement regarding the current Fleischner Society CT criteria for UIP was moderate to substantial among raters with varying levels of experience. There was only slight to fair agreement regarding the diagnostic hypotheses and for the level of confidence in the most likely diagnosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".