Mortality in ASIA Impairment Scale grade A to D Patients With Odontoid Fracture and Magnetic Resonance Imaging Evidence of Spinal Cord Injury
Bibliographic record
Abstract
Odontoid fractures are common, often presenting in the elderly after a fall and infrequently associated with traumatic spinal cord injury (tSCI). The goal of this study was to analyze predictors of mortality and neurological outcome when odontoid fractures were associated with signal change on magnetic resonance imaging (MRI) at admission. Over an 18-year period (2001–2019), 33 patients with odontoid fractures and documented tSCI on MRI were identified. Mean age was 65.3 years (standard deviation [SD] = 17.2), and 21 patients were male. The mechanism of injury was falls in 25 patients, motor vehicle accidents in 5, and other causes in 3. Mean Injury Severity Score (ISS) was 40.5 (SD = 30.2), Glasgow Coma Scale (GCS) score was 13 (SD = 3.4), and American Spinal Injury Association (ASIA) motor score (AMS) was 51.6 (SD = 42.7). ASIA Impairment Scale (AIS) grade was A, B, C, and D in 9, 2, 3, and 19 patients, respectively. Mean intramedullary lesion length was 32.3 mm (SD = 18.6). The odontoid peg was displaced ventral or dorsal in 15 patients. Twenty patients had surgical intervention: anterior odontoid screw fixation in 7 and posterior spinal fusion in 13. Eleven (33.3%) patients died in this series: withdrawal of medical care in 5; anoxic brain injury in 4; and failure of critical care management in 2. Univariate logistic regression indicated that GCS score ( p < 0.014), AMS ( p < 0.002), AIS grade ( p < 0.002), and ISS ( p < 0.009) were risk factors for mortality. Multi-variate regression analysis indicated that only AMS ( p < 0.002) had a significant relationship with mortality when odontoid fracture was associated with tSCI (odds ratio, 0.963; 95% confidence interval, 0.941–0.986).
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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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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".