Traumatic Brain Injury in Spinal Cord Injury: Frequency and Risk Factors
Bibliographic record
Abstract
BACKGROUND: The frequency of traumatic brain injury (TBI) co-occurring with traumatic spinal cord injury (tSCI) is unclear despite a number of past studies; as well, limited research has examined predictors of co-morbid TBI in tSCI patients. OBJECTIVES: (1a) To summarize past literature on comorbid diagnosis of TBI in tSCI in order to reexamine the frequency of dual diagnosis in a study designed to obviate past methodological limitations; (1b) to compare dual-diagnosis frequency with vs without the inclusion of diagnostically ambiguous cases; and (2) to measure risk factors for tSCI and comorbid TBI. METHODS: Ninety-one of 135 eligible adults with tSCI, 3 to 6 months postinjury, were prospectively recruited from a tertiary inpatient tSCI rehabilitation program. TBI diagnosis was based on comprehensive, validated clinical neurological and neuroimaging measures. RESULTS: Objective 1: 39.6% of the tSCI patients sustained a concomitant TBI, but when ambiguous cases were removed from analysis, frequency rose to 58.1%. Objective 2: Motor vehicle collisions were most likely to yield a comorbid TBI diagnosis, but 31.6% of falls also resulted in TBI. Patients with cervical and thoracic injuries showed a very similar frequency of comorbid TBI. CONCLUSIONS: Varied methodological approaches, particularly the decision to include/exclude ambiguous cases, likely explain disparate past estimates of TBI in tSCI. However, even this study's lower frequency estimate, at nearly 40%, is clinically important. The prevailing assumption that dual diagnosis is less common in thoracic than cervical spine injuries was not supported. Finally, while comorbid TBI most frequently occurred in motor vehicle collisions, nearly a third of tSCIs sustained in falls resulted in comorbid TBI in our sample.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".