People with HIV at the end-of-life and their next-of-kin/loved ones are willing to participate in interventional HIV cure-related research
Bibliographic record
Abstract
INTRODUCTION: The Last Gift study at the University of California San Diego (UCSD), United States enrolls terminally ill people with HIV (PWH) in HIV cure research. METHODS: From 2017 to 2022, we conducted surveys with Last Gift participants and their next-of-kin/loved ones to evaluate willingness to participate in different types of HIV cure research at the end of life (EOL). We analyzed willingness data descriptively. RESULTS: We surveyed 17 Last Gift participants and 17 next-of-kin/loved ones. More than half of Last Gift participants ( n = 10; 58.8%) expressed willingness to participate in studies involving totally new treatments or approaches ('first-in-human' studies), a combination of different approaches, the use of unique antibodies, proteins or molecules, or therapeutic vaccines. Under one-quarter of Last Gift participants ( n = 4; 23.5%) expressed willingness to participate in research involving interventions that may shorten their life expectancy to benefit medical research. Most Last Gift participants and their next-of-kin/loved ones also expressed high acceptance for various types of donations and biopsies at the EOL (e.g. hair donations and skin, lymph node or gut biopsies). DISCUSSION: Knowing whether people would be willing to participate in different types of EOL HIV cure research can help inform the design of future innovative studies. As a research community, we have a duty to design studies with adequate safeguards to preserve the public trust in research and honor PWH's important gift to humanity.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".