Vaccine trials during a pandemic: potential approaches to ethical dilemmas
Bibliographic record
Abstract
Ever since the emergence of the coronavirus disease 2019 (COVID-19), global public health infrastructures and systems, along with community-wide collaboration and service, have risen to an unprecedented challenge. Vaccine development was immediately propelled to the centre of all our scientific, public health and community efforts. Despite the development of SARS-CoV-2 vaccines arguably being the greatest and most palpable achievements of the past 12 months, they have also been one of the most contentious and debated issues during the pandemic. However, what uniquely differentiates vaccine development is its intimate relationship with the community it seeks to serve; both in its clinical trial testing as an efficacious and safe prophylactic, and its post-developmental 'roll-out' success, as an effective public health tool. These relationships have birthed a myriad of complexities, from community-based mistrust, to academically contended ethical dilemmas. Indeed, the accelerated advances in the COVID-19 vaccine race have further exacerbated this phenomenon, bringing with it new ethical dilemmas that need to be examined to ensure the continued clinical success of these therapeutics and a renewed societal trust in clinical medicine.In this paper, we discuss two major ethical dilemmas: (1) the equipoise of continuing new vaccine trials in the advent of successful candidates and (2) the maleficence of blinded placebo arms. Accordingly, we discuss six different potential approaches to these ethical dilemmas: (1) continuing with placebo-controlled trials, (2) transitioning from placebo-controlled to open-label, (3) unblinding at-risk priority groups only, (4) transitioning to a blinded stepped-wedge cross-over design, (5) progressing to a blinded active-controlled stepped-wedge cross-over trial, and (6) conducting randomised stepped-wedge community trials. We also propose a decision-making algorithm for relevant stakeholders in advanced stages of vaccine trials.It is important to remember that the emergent nature of the COVID-19 situation does not justify a compromise on core ethical values. In fact, the discourse surrounding this topic and the decisions made will remain a potent case study and a continuously referenced example for all such future scenarios.
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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 | 0.583 | 0.606 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.038 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".