The mortality burden of hypersensitivity pneumonitis across Europe
Bibliographic record
Abstract
Background: Population level mortality data from Hypersensitivity Pneumonitis (HP) are limited, especially across Europe. We aimed to estimate the mortality burden from HP across individual countries in Europe as well as a continent overall between 2011 and 2018. Methods: We extracted information on age and sex stratum specific registered deaths from EUROSTAT, the official statistical office of the European Union between the years 2011 and 2018. We calculated age and sex-standardised mortality rates, standardised to the 2018 European population for each individual countries in turn, as well as an overall European estimate. Results: Standardised mortality rates for Europe increased from 0.06 per 100,000 person years (95% Confidence Interval [CI] 0.05-0.07) in 2011 to 0.12 per 100,000 person years in 2018 (95% CI 0.11-0.13). We observed geographical variation in standardised mortality rates, with Portugal having the highest age and sex-standardised mortality rate, followed by the United Kingdom. In contrast, there were no recorded deaths from HP in Iceland and Greece across the study period (Figure 1). Figure 1: Overall age and sex standardized mortality rates from Hypersensitivity Pneumonitis erj;66/suppl_69/PA4030/F1 F1 F1 Conclusion: Mortality rates from HP in Europe doubled between 2011 and 2018, with significant inter-country variation. Despite this, the overall European HP mortality rate is low, especially in comparison to idiopathic pulmonary fibrosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".