Decompressive Craniectomy: Contralateral Lesions and Metabolic Abnormalities
Bibliographic record
Abstract
OBJECTIVE: To define the incidence of new contralateral intracranial lesions following decompressive hemicraniectomy for blunt traumatic brain injury, and explore the potential association with metabolic factors that contribute to coagulopathy. METHODS: We retrospectively reviewed the records and imaging of all patients treated with hemicraniectomy for blunt traumatic brain injury at our institution from May 2007 up to and including January 2012. RESULTS: Twenty patients were identified during the time period to have undergone decompressive craniectomy for blunt head injury. The average age and Glasgow Coma Scale on presentation was 44.1 years (range: 19 – 72 years) and 6.5 (range: 3 – 14) respectively. All but one patient presented with an extra-axial hematoma as their surgical indication for craniectomy. Seven patients (35.0%) developed new contralateral lesions post-craniectomy. The average peri-operative pH, bicarbonate (HCO₃) and hematocrit (HCT) levels for those with new contralateral lesions were lower than those without new lesions. Five of the seven patients (71.4%) with new lesions had abnormalities on their laboratory results that have been know to be attributable to coagulopathy, with four (57.1%) having two or more abnormal results. Eight of 13 (61.5%) patients without new lesion had laboratory abnormalites, with five (38.5%) having two or more abnormalities identified. CONCLUSIONS: The incidence of new contralateral lesions post-craniectomy for blunt head injury is 35.0% in our experience. There is an association between the metabolic derangements linked to trauma related coagulopathy and the formation of new lesions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".