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Record W2159255260 · doi:10.1177/2150129711408306

Have Technological Innovations Made Unethical the Use of Human Subjects for Potency Assessments of Allergenic Extracts?

2011· article· en· W2159255260 on OpenAlexafffund
Jason Behrmann

Bibliographic record

VenueJournal of Asthma & Allergy Educators · 2011
Typearticle
Languageen
FieldMedicine
TopicAllergic Rhinitis and Sensitization
Canadian institutionsUniversité de Montréal
FundersUniversité de Montréal
KeywordsStandardizationPotencyMedicineRisk analysis (engineering)Consistency (knowledge bases)Subject (documents)BiotechnologyEngineering ethicsPolitical scienceComputer scienceLawEngineeringBiology

Abstract

fetched live from OpenAlex

Since the 1990s, production batch consistency and the standardization of potency units of allergenic extracts used in allergen immunotherapy has been the focus of drug regulatory reforms and much academic debate. This article seeks to expand the current debate by identifying ethical arguments in support of regulatory reforms to eliminate the use of human subjects for potency assessments of these therapeutics. Although human subject testing is the best method to assess biological potency, it also exposes subjects to significant risks, risks that ought to be avoided as much as possible. Innovation in in vitro immunoassays will soon provide feasible alternatives to biological assessments. This article argues that the allergology community must now consider eliminating human subjects in standardization and potency assessment methods as an ethical imperative in regulatory reforms. Moreover, the allergology community will soon need to reach consensus regarding when in vitro tests are “good enough” in replicating biological potency assessments so that human subject testing could be avoided without compromising the safety and efficacy of allergen immunotherapy. Overall, this discussion will provide an overview on how to structure global standardization regulations for allergenic extracts based on the principle of minimizing human subject testing, a topic that, to date, has been largely overlooked in relation to extract standardization policies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.104
GPT teacher head0.342
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2011
Admission routes2
Has abstractyes

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