Late Breaking Abstract - Multi-center validation of the Hypersensitivity pneumonitis South Asian questionnaire for Exposure
Bibliographic record
Abstract
Introduction: We developed the Hypersensitivity Pneumonitis South Asian Questionnaire for Exposure (HP-SAQE), a novel tool to systematically identify environmental and occupational exposures in patients with interstitial lung disease (ILD), particularly HP (ERS abstract 95). This multicenter study aimed to prospectively validate the HP-SAQE using multidisciplinary discussion (MDD) as the diagnostic gold standard. Methods: Patients with ILD were prospectively enrolled across five Indian centers, each participant completed the HP-SAQE in their native language. The questionnaire consisted of two components: a qualitative section listing various environmental exposures, and a quantitative section comprising six elements used to generate an exposure score for each item, ranging from 0 to 14. For each case, an online MDD panel reviewed clinical, radiological, and histopathological data to assign a final diagnosis and level of confidence. Results: A relevant HP-related exposure was identified in 98.5% of HP cases and 62.5% of non-HP ILD cases. The exposure score above 7.5 demonstrated good diagnostic performance in distinguishing patients with HP from those with non-HP ILD, with an area under the ROC curve of 0.778 (p<0.001). Conclusion: The HP-SAQE is a validated, regionally tailored instrument. Its multicenter validation and robust diagnostic performance support its integration into routine multidisciplinary ILD assessment. erj;66/suppl_69/PA6139/F1 F1 F1
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".