The Use and Misuse of Penh in Animal Models of Lung Disease
Bibliographic record
Abstract
Models of the outer epithelia of the human body - namely the skin, the intestine and the lung - have found valid applications in both research and industrial settings as attractive alternatives to animal testing. A variety of approaches to model these barriers are currently employed in such fields, ranging from the utilization of ex vivo tissue to reconstructed in vitro models, and further to chip-based technologies, synthetic membrane systems and, of increasing current interest, in silico modeling approaches. An international group of experts in the field of epithelial barriers was convened from academia, industry and regulatory bodies to present both the current state of the art of non-animal models of the skin, intestinal and pulmonary barriers in their various fields of application, and to discuss research-based, industry-driven and regulatory-relevant future directions for both the development of new models and the refinement of existing test methods. Issues of model relevance and preference, validation and standardization, acceptance, and the need for simplicity versus complexity were focal themes of the discussions. The outcomes of workshop presentations and discussions, in relation to both current status and future directions in the utilization and development of epithelial barrier models, are presented by the attending experts in the current report.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".