Brain death rates in severe blunt traumatic brain injury: comparison of decompressive craniectomy to a medically managed cohort
Bibliographic record
Abstract
Introduction: Decompressive craniectomy (DC) in severe traumatic brain injury (TBI) is controversial. The impact DC on cause of death is unclear in the literature to date. Methods: We performed an institutional retrospective review, from June 2003 to June 2013, of patients with severe blunt TBI undergoing DC whom subsequently died. We compared this group to a retrospectively matched cohort based: age, pre-hospital mRS, Marshall diffuse and TBI grades, Injury Severity Scores, and admission laboratory values. The goal was to determine the cause of death between those receiving DC and those managed medically. Results: Nineteen patients received DC and were compared to 16 retrospectively matched patients. The mean age of the DC and matched cohort were 47.1 and 43.6 years, respectively. The mean admission GCS/Marshall diffuse CT grades were 5.8/3.4 for the DC group, and 4.1/3.1 for the matched medical cohort. Overall, in the DC group 94.7% of the deaths occurred secondary to cardiac arrest after withdrawal of life sustaining treatment (WLST), with only 5.3% progressing to brain death. Alternatively, in the matched cohort 62.5% died of cardiac arrest post WLST, with 37.5% progressing to brain death. Conclusions: Progression to brain death appears to be more common in those severe blunt TBI patients treated medically compared to those undergoing DC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".