Evidence map›Paper›PMID 38159935›Full record

ArticleJournal of medical ethics2026

Exploring the ethics of tuberculosis human challenge models.

Abie Rohrig, Josh Morrison, Gavriel Kleinwaks, Jonathan Pugh, Helen McShane, Julian Savulescu

Abstract read
In one paragraph

Article in Journal of medical ethics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Trial
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Abie RohrigColumbia University, New York, New York, USA.ORCID http://orcid.org/0000-0002-7258-5824
Josh Morrison1Day Sooner, Baltimore, Maryland, USA.
Gavriel Kleinwaks1Day Sooner, Baltimore, Maryland, USA.
Jonathan PughOxford Uehiro Centre for Practical Ethics, University of Oxford, Oxford, UK jonathan.pugh@philosophy.ox.ac.uk.
Helen McShaneJenner Institute, University of Oxford Nuffield Department of Medicine, Oxford, Oxfordshire, UK.
Julian SavulescuOxford Uehiro Centre for Practical Ethics, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-1691-6403

Funding

Wellcome Trust 104848Wellcome Trust 203132Wellcome Trust 206331/Z/17/Z
6 · The paper itself

Abstract

We extend recent conversation about the ethics of human challenge trials to tuberculosis (TB). TB challenge studies could accelerate vaccine development, but ethical concerns regarding risks to trial participants and third parties have been a limiting factor. We analyse the expected social value and risks of different challenge models, concluding that if a TB challenge trial has between a 10% and a 50% chance of leading to the authorisation and near-universal delivery of a more effective vaccine 3-5 years earlier, then the trial would save between 26 400 and 1 100 000 lives over the next 10 years. We also identify five important ethical considerations that differentiate TB from recent human challenge trials: an exceptionally high disease burden with no highly effective vaccine; heightened third party risk following the trial, and, partly for that reason, uniquely stringent biosafety requirements for the trial; risks associated with best available TB treatments; and difficulties with TB disease detection. We argue that there is good reason to consider conducting challenge trials with attenuated strains like Bacillus Calmette-Guérin or attenuated

Indexed as

Clinical Trials as TopicTuberculosisTuberculosis VaccinesVaccine DevelopmentBCG VaccineHumansMycobacterium tuberculosisBCG VaccineTuberculosis Vaccinesclinical trialcommunicable diseasesethics- research

Identifiers

PMID38159935
PMCPMC7617737

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.