Alternatives to HIST for acellular pertussis vaccines: progress and challenges in replacement.
Bibliographic record
Abstract
The 'International Workshop on Alternatives to the Murine Histamine Sensitization Test for Acellular Pertussis Vaccines: Progress and Challenges in the Replacement of HIST' was held on 24 August 2014, in Prague, Czech Republic, as a satellite meeting to the 9th World Congress on Alternatives and Animal Use in the Life Sciences. Participants discussed the progress and challenges associated with the development, validation, and implementation of in vitro assays as replacements for the histamine sensitisation test (HIST) for acellular pertussis vaccines. Discussions focused on the consistency approach, the necessary framework for regulatory acceptance of a harmonised method, and recent international efforts towards the development of in vitro assays to replace the HIST. Workshop participants agreed that acceptable alternatives to the HIST should be based on ADP ribosylation-mediated cell intoxication and therefore that the CHO cell clustering assay, which measures cell intoxication, should be further pursued and developed as a possible replacement for the HIST. Participants also agreed to continue ongoing multinational discussions involving national and international standardisation authorities to reach consensus and to organise collaborative studies in this context for assay characterisation and calibration of reference materials.
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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 | 0.031 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".