Prognostication and Goals of Care Decisions in Severe Traumatic Brain Injury: A Survey of The Seattle International Severe Traumatic Brain Injury Consensus Conference Working Group
Bibliographic record
Abstract
onference (SIBICC) participated in a survey consisting of 24 questions. Questions queried use of prognostic calculators, variability in and responsibility for goals of care decisions, and acceptability of neurological outcomes, as well as putative means of improving decisions that might limit care. A total of 97.6% of the 42 SIBICC panelists completed the survey. Responses to most questions were highly variable. Overall, panelists reported infrequent use of prognostic calculators, and observed variability in patient prognostication and goals of care decisions. They felt that it would be beneficial for physicians to improve consensus on what constitutes an acceptable neurological outcome as well as what chance of achieving that outcome is acceptable. Panelists felt that the public should help to define what constitutes a good outcome and expressed some support for a "nihilism guard." More than 50% of panelists felt that if it was certain to be permanent, a vegetative state or lower severe disability would justify a withdrawal of care decision, whereas 15% felt that upper severe disability justified such a decision. Whether conceptualizing an ideal or existing prognostic calculator to predict death or an unacceptable outcome, on average a 64-69% chance of a poor outcome was felt to justify treatment withdrawal. These results demonstrate important variability in goals of care decision making and a desire to reduce this variability. Our panel of recognized TBI experts opined on the neurological outcomes and chances of those outcomes that might prompt consideration of care withdrawal; however, imprecision of prognostication and existing prognostication tools is a significant impediment to standardizing the approach to care-limiting decisions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".