Bibliographic record
Abstract
To the Editor: We read with interest the review ‘When does a human being die?’1 Opinions vary on this topic. According to Gary Gronseth, M.D., professor and vice chair of neurology at the University of Kansas Medical Center, ‘brain death is as valid a definition of death, as if your heart had stopped beating. If you're brain dead, you're dead’.2 Brainstem death results in no brain stem activity and causes permanent loss in consciousness and the capacity to breathe. Catastrophic brain injury refers to acute severe brain injury, with intracranial bleeding or cerebral contusions that may lead to death. In boxing, about 10 deaths per year have occurred during the twentieth century; most related to knockout or technical knockout. The most common cause of death is subdural hematoma.3 During infectious diseases fellowship, one of us consulted a 25-year-old African American boxer who had catastrophic brain injury and a fever (40°C). Knocked out in an amateur boxing match, this patient went back to his training room and collapsed. On admission, he was comatose (Glasgow Coma Score = 3). For all intents and purposes, this patient was brain dead when he came to the hospital or even in the locker room. Yet, by using an organismic, denouement definition of death, as Schofield et al. propose, this subject would still be considered alive. The search for the source of fever was futile, since this patient’s brain thermoregulatory center was scrambled beyond repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.029 | 0.046 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".