Primary or Secondary Decompressive Craniectomy: Different Indication and Outcome
Bibliographic record
Abstract
BACKGROUND: Intracranial hypertension can cause secondary damage after a traumatic brain injury. Aggressive medical management might not be sufficient to alleviate the increasing intracranial pressure (ICP), and decompressive craniectomy (DC) can be considered. Decompressive craniectomy can be divided into categories, according to the timing and rationale for performing the procedure: primary (done at the time of mass lesion evacuation) and secondary craniectomy (done to treat refractory ICP). Most studies analyze primary and secondary DC together. Our hypothesis is that these two groups are distinct and the aim of this retrospective study is to evaluate the differences in order to better predict outcome after DC. METHODS: Seventy patients had DC over a period of four years at our center. They were divided into two groups based on the timing of the DC. Primary DC (44 patients) was done within 24 hours of the injury for mass lesion evacuation. Secondary DC (26 patients) was done after 24 hours and purely for the treatment of refractory ICP. Pre-op characteristics and post-op outcomes were compared between the two groups. RESULTS: There was a significant difference in the mechanism of injury, the pupil abnormalities and Marshall grade between primary and secondary DC. There was also a significant difference in outcome with primary DC showing 45.5% good outcome and 40.9% mortality and secondary DC showing 73.1% good outcome and 15.4% mortality. CONCLUSIONS: Primary and secondary DC have different indications and patients characteristics. Outcome prediction following DC should be adjusted according to the surgical indication.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".