C.07 Door to decompression should be the benchmark in trauma craniotomies
Bibliographic record
Abstract
Background: Quality control indicators for mass lesion in TBI use the delay between emergency department (ED) and OR arrival to measure quality of care. It does not provide the timing of brain decompression. The goals of this study are to observe step by step where delays occur from hospital admission until effective decompression of the brain. Methods: A prospective observational data collection of timing from ED admission to decompression was conducted for all emergency trauma craniotomies over a period of 15 months. Results: Sixty-five patients were included. Doing a CT at the outside institution instead of transferring the patient prior to CT resulted in a 112min delay in care. Neurosurgery team notification prior to patient’s arrival to ED shortened delivery of care by 51min. The time elapsed between OR arrival and brain decompression was 50min: anesthesia time 3min, surgical positioning/preparation 29min and surgical time 17min. Burrhole decompression followed by craniotomy (9min) shortened the decompression time by 17min compared to standard 4 holes craniotomy approach (26min). Conclusions: Benchmark for trauma system performance in emergency craniotomies should be door to decompression time. Bypassing CT in local hospitals, pre-alerting neurosurgeons, and burrhole decompression followed by standard craniotomy significantly decrease door to decompression time.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".