Hemorrhagic complications after decompressive craniectomy
Bibliographic record
Abstract
BACKGROUND: Decompressive craniectomy (DC) is the preferred surgical management option for lowering refractory intracranial pressure in cases of traumatic brain injury (TBI). A number of randomized controlled trials have demonstrated decreased mortality but increased morbidity following DC for TBI patients. Here, we reviewed the frequency of postoperative hemorrhagic complications following DC correlating with poor outcomes. METHODS: We retrospectively reviewed the medical records of patients who presented with TBI and underwent DC during the years 2015-2017. The frequency and characteristics of hemorrhagic complications were correlated with the patients' outcomes. RESULTS: There were 74 patients with TBI included in the study who underwent DC. Of these, 31 patients developed expansion of existing hemorrhagic lesions, 13 had new contusions, three developed new extradural hemorrhages, two developed new subdural hematomas, and one patient developed an intraventricular hemorrhage. Those who developed expansion of existing hemorrhagic lesions following DC had longer ICU stays and poorer outcomes (Glasgow outcome scale). CONCLUSION: After 74 DC performed in TBI patients, 67% developed new hemorrhagic lesions or expansion of previously existing hemorrhages. This finding negatively impacted clinical outcomes, including mortality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".