Bibliographic record
Abstract
We read with interest the recent commentary by Bernat and Delmonico1 entitled “Restoring Activity of Pig Brain Cells After Death Does Not Invalidate the Determination of Death by Neurologic Criteria or Undermine the Propriety of Organ Donation After Death.” We were particularly interested in the algorithm that outlines the entirety of donation progress possibilities (Figure 11 of that publication). While we feel that this algorithm offers a generally comprehensive and cohesive overview of donation pathways, we would like to suggest 1 addition. As first pioneered in Belgium and the Netherlands, and later in Canada, there is an additional entry point onto the donation after circulatory determination of death pathway: donation after medical assistance in dying (MAID)—often referred to as voluntary euthanasia.2,3 In our province of Québec, we have increasingly incorporated this pathway to donation into end–of–life care, with over 20 completed cases of donation after MAID since 2017 resulting in 64 transplanted organs (internal Transplant Québec reports). This currently represents 5%–10% of our total donation activity. This practice has been met with general acceptance from stakeholders including MAID providers, donation professionals, and the general public, including several favorable media reports.4 As other countries consider MAID legislation, it is important not to forget that this pathway can provide a source of transplanted organs while fulfilling the patient’s intent to help others at the end of their own life. We thus suggest that this pathway be added to future editions of your otherwise extremely informative algorithm.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.060 | 0.112 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".