Medical Assistance in Death as a Unique Opportunity to Advance Human Immunodeficiency Virus Cure Research
Bibliographic record
Abstract
The inability to sample deep-tissue reservoirs in individuals living with human immunodeficiency virus (HIV) has greatly hindered accurate estimates of viral reservoir size and distribution. Animal models and collection of tissues during autopsies of HIV-positive individuals are 2 proposed solutions to this problem. Each, however, has its limitations. In this Viewpoint, we argue that tissue donation following medical assistance in death (MAiD) will form an invaluable resource for the characterization of the viral reservoir in the context of current HIV cure research. In support, we discuss a recent instance in which an individual living with HIV chose to donate their body/tissues to HIV research prior to undergoing MAiD at our institution. Going forward, we hope this will help provide support to individuals in their decisions around tissue donation following MAiD, while highlighting how healthcare providers, by complying with such wishes, can affect patient satisfaction in the last days of life.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".