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Record W3010453467 · doi:10.1111/bioe.12736

What risks should be permissible in controlled human infection model studies?

2020· article· en· W3010453467 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioethics · 2020
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRisk analysis (engineering)Risk assessmentActuarial sciencePsychologyMedicineBusinessEconomics

Abstract

fetched live from OpenAlex

Controlled human infection model (CHIM) studies involve the intentional exposure of healthy research volunteers to infectious agents. These studies contribute to knowledge about the cause or development of disease and to the advancement of vaccine research. But they also raise ethical questions about the kinds of risks that should be permissible and whether limits should be imposed on research risks in CHIM studies. Two possible risk thresholds have been considered for CHIM studies. The first suggests constraining ethically permissible risks according to a minimal risk threshold and the second endorses a higher risk threshold that excludes irreversible or fatal infections. I argue that neither of these thresholds is persuasive and situate questions about risk thresholds in CHIM studies within a broader debate about permissible risks in research. I argue that risks in CHIM studies should be constrained according to limits on research risks that do not offer corresponding benefits in all studies rather than developing a unique risk threshold for CHIM studies. I then propose five recommendations for the ethical assessment of risk in CHIM studies.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
opusMetaresearch
Domain: Methods · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.005
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.947
GPT teacher head0.708
Teacher spread0.239 · 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