Identification of Diagnostic Criteria for Chronic Hypersensitivity Pneumonitis. An International Modified Delphi Survey
Bibliographic record
Abstract
RATIONALE: Current diagnosis of chronic hypersensitivity pneumonitis (cHP) involves considering a combination of clinical, radiological, and pathological information in multidisciplinary team discussions. However, this approach is highly variable with poor agreement between centers. OBJECTIVES: We aimed to identify diagnostic criteria for cHP that reach consensus among international experts. METHODS: A 3-round modified Delphi survey was conducted between April and August 2017. Forty-five experts in interstitial lung disease from 14 countries participated in the online survey. Diagnostic items included in round 1 were generated using expert interviews and literature review. During rounds 1 and 2, experts rated the importance of each diagnostic item on a 5-point Likert scale. The a priori threshold of consensus was ≥ 75% of experts rating a diagnostic item as very important or important. In the third round, experts graded the items that met consensus as important and provided their level of diagnostic confidence for a series of clinical scenarios. MEASUREMENTS AND MAIN RESULTS: Consensus was achieved on 18 of the 40 diagnostic items. Among these, experts gave the highest level of importance to the identification of a causative antigen, time relation between exposure and disease, mosaic attenuation on chest imaging, and poorly formed non-necrotizing granulomas on pathology. In clinical scenarios, the diagnostic confidence of experts in cHP was heightened by the presence of these diagnostic items. CONCLUSION: This consensus-based approach for the diagnosis of cHP represents a first step towards the development of international guidelines for the diagnosis of cHP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".