Prognostic Value of Age and Early Magnetic Resonance Imaging in Patients with Cervical Subaxial Spinal Cord Injuries
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The predictive role of a patient's age in spinal cord injury (SCI) is still unclear given the coexistence of potential confounding factors, whether clinical or radiological. Thus, it is the aim of this work to assess the prognostic role of a patient's age against initial radiological features in a traumatic cervical SCI population. METHODS: Clinical and radiological data from patients with acute traumatic cervical SCI and a first MRI performed within 48 h of trauma were retrospectively reviewed. Patients were dichotomized according to the length intramedullary lesion, and associations between age and other clinical or radiological prognostic variables were analyzed. The receiver-operating characteristic (ROC) curve was used to test the discriminative capacity of the patient age to predict neurological and functional outcomes. Poor functional outcome was defined as a Walking Index Spinal Cord Injury score <1 and poor neurological outcome as the lack of neurological improvement between admission and follow up. RESULTS: 134 patients fulfilled the inclusion criteria and were analyzed. The mean age was 43 years, with a male/female ratio of 4:1. polytrauma and soft tissue injuries were inversely proportional to patient age (P < 0.001). A critical value of 55-year-old was established as a threshold for determining poor functional and neurological outcomes. Across the group of patients with minor intramedullary lesions, older age was correlated with poor functional and neurological outcomes (P < 0.001 and P = 0.04, respectively). CONCLUSIONS: Patient age is an important prognostic factor in patients with traumatic cervical SCI. Fifty-five years is the critical cutoff associated with poor prognostic outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".