Clival fractures in a Level I trauma center
Bibliographic record
Abstract
OBJECT: Clival fracture (CF) is rare among head traumas. The aim of this study was to explore how radiological features observed in CF reflect the clinical picture and mechanism of injury in such cases. METHODS: Radiological data for patients with skull base fracture admitted to the Montreal General Hospital between February 2002 and October 2012 were obtained from the Quebec Trauma Registry and reviewed for CF. Identified CF was categorized by orientation and quality. Injury mechanism, clinical presentation, and follow-up outcome were obtained through retrospective chart review. RESULTS: Of the 1738 patients with skull base fractures, 65 exhibited CF, representing 1.2% of the 5416 patients with traumatic brain injuries admitted during the period studied. Thirty-nine (60%) of the 65 CFs were obliquely oriented, 17 (26.2%) were longitudinal, and 9 (14%) were transverse. Twenty-nine (45%) of the 65 patients demonstrated linear fracture, 17 (26%) hairline, 10 (15%) diastatic, and 9 (14%) displaced. Cranial nerve deficits and vascular injury occurred in 13.8% and 7.7% of cases, respectively. Twenty-five patients (38.5%) died in hospital. The long-term Extended Glasgow Outcome Scale score was significantly lower in transverse compared with longitudinal and oblique fractures (p = 0.03 and 0.03, respectively) and lower in diastatic compared with displaced fractures (p = 0.05). CONCLUSIONS: This study provides information on the largest CF population studied to date, expands the current CF classification to include fracture quality as well as orientation, and underscores the existence of significant differences in pathogenesis and clinical presentation of CF subtypes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.010 | 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".