Local Lymph Node Data for the Evaluation of Skin Sensitization Alternatives: A Second Compilation
Bibliographic record
Abstract
BACKGROUND: Development, evaluation and validation of alternatives to skin sensitisation testing require the availability of reliable databases with which comparative analyses can be conducted to establish performance characteristics. To facilitate this we have published previously a database comprising results from local lymph node assays (LLNAs) conducted with 211 chemicals. That database embraced a substantial range of chemistry, and of relative skin sensitising potency, and has found application in the assessment of new or refined methods. OBJECTIVE: In this paper we describe a second compilation to extend the LLNA database. METHODS: This second data compilation was derived from previously conducted LLNA studies involving an additional 108 chemicals. In addition, the first database contained a small number of inaccuracies, affecting results recorded with a few chemicals. In this paper these have been corrected. RESULTS: The inclusion of 108 new substances has served to extend and consolidate the areas of chemistry covered by the database. In addition, the entire dataset was evaluated for pre and prohaptens which will facilitate the choice of chemicals for alternative assay developments. CONCLUSIONS: It is anticipated that the new revised and extended database totalling over 300 chemicals will now serve as the primary resource to support the development and evaluation of new approaches to hazard identification and potency assessment.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".