Development of a Knowledge-sharing Website for Hypersensitivity Pneumonitis Exposures
Bibliographic record
Abstract
Background: Identification of potential inciting antigens known to be associated with hypersensitivity pneumonitis (HP) is integral to achieving a confident diagnosis, its management, and prognostication. A barrier to achieving diagnostic confidence is that evidence for exposure assessment is not presented in an easily accessible or useful format. Objective: To develop a freely available comprehensive living repository of contemporaneous exposures associated with HP. Methods: . Articles were included if they described adults or children with a diagnosis of HP (as defined by authors) and a description of the causative exposure. Website development used a structured query language database and was powered by WordPress. Results: HPLung.com is a freely available online searchable repository that lists all exposures and antigens associated with HP and links to their parent studies. Eighty-five unique exposures from 447 citations have been included thus far. The website continues to be updated iteratively. Since its launch in 2019, HPLung.com has been visited over 4,500 times by users from 75 different countries. Conclusion: HPLung.com is a novel knowledge-sharing tool that addresses barriers of accessibility, contemporaneity, efficient evidence synthesis, and making the best use of technological platforms to improve the exposure assessment of those suspected of HP.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.020 |
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".