Risk of post-operative cerebrospinal fluid leak and mortality in surgically managed traumatic brain injury patients: a single centre Canadian experience
Bibliographic record
Abstract
BACKGROUND: Cerebrospinal fluid (CSF) leaks pose significant risks to post-operative neurosurgical patients. has been limited investigation into post-operative CSF leak in trauma patients. The current study aims to provide an overview of the experience at a Canadian Level 1 Trauma Centre with neurosurgically managed traumatic brain injury (TBI) to improve understanding of prognostic factors for development of CSF leak and mortality among these patients. METHODS: A retrospective cohort study was performed at Hamilton General Hospital, a Level 1 Trauma Centre in Hamilton, Ontario. Univariate analyses were performed to determine potential prognostic factors for CSF leak and mortality. A multivariable analysis was conducted to determine prognostic factors for mortality among this cohort. RESULTS: A total of 211 patients were included in the analyses. Of these, 16 patients developed post-operative CSF leak. Univariate analyses determined fracture repair, presence of subdural haematoma or depressed skull fracture, penetrating injuries, mild TBI, increasing pre-operative midline shift, and re-operation were found to independently increase the risk of CSF leak. In-hospital mortality in our cohort was 36%. In univariate analyses increasing age, presence of subdural haematoma, pedestrian versus vehicle collision as mechanism of injury, and TBI severity were independently associated with mortality. In the multivariable analysis, only age and presenting GCS were found to significantly increase odds of mortality among our population. CSF leak was associated with a nearly 4-fold increase in odds of death, however this finding was not statistically significant. CONCLUSION: Age and severity of TBI are important predictors of mortality in neurosurgically managed TBI patients. CSF leak may be an important predictor of mortality, warranting further investigation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".