Ethical Guidance for Neuroprognostication in Disorders of Consciousness
Bibliographic record
Abstract
In the United States, 4.8 million individuals present to the emergency department for traumatic brain injury (TBI) evaluation and 2.9 million are diagnosed with TBI every year. Approximately 224,000 are hospitalized due to injury severity.1 Recent studies have demonstrated variability and conflicts in decision-making surrounding withdrawal or limitation of care (WLC). One study that looked at a decade of deaths (54% related to WLC) at a level 1 trauma center in the United States found decision-making conflicts between physicians and family (57%), between family members (33%), and between patients and family members (9.5%).2 Although 68% of WLC cases were from a similar US level 1 trauma treatment center study, there were differences in TBI severity (64% for severe TBI and 92% for moderate TBI) and both within-center and between-center variation in decision-making.3 Finally, although about 70% of deaths were associated with WLC in a 2-year retrospective cohort study at 6 Canadian level 1 trauma centers, there was variability between treatment centers (45%–87%).4 These studies demonstrate an international trend for variability in treatment and conflicts in decision-making regarding WLC for patients with disorders of consciousness (DoC).
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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 | 0.089 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.024 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".