Functional Outcome Prediction after Traumatic Spinal Cord Injury Based on Acute Clinical Factors
Bibliographic record
Abstract
Spinal cord injury (SCI) is a devastating condition that affects patients on both a personal and societal level. The objective of the study is to improve the prediction of long-term functional outcome following SCI based on the acute clinical findings. A total of 76 patients with acute traumatic SCI were prospectively enrolled in a cohort study in a single Level I trauma center. Spinal Cord Independence Measure (SCIM) at 1 year after the trauma was the primary outcome. Potential predictors of functional outcome were recorded during the acute hospitalization: age, sex, level and type of injury, comorbidities, American Spinal Injury Association (ASIA) Impairment Scale (AIS), ASIA Motor Score (AMS), ASIA Light Touch score (LT), ASIA Pin Prick score (PP), Injury Severity Score (ISS), traumatic brain injury, and delay from trauma to surgery. A linear regression model was created with the primary outcome modeled relative to the acute clinical findings. Only four variables were selected in the model, with performance averaging an R-square value of 0.57. In descending order, the best predictors for SCIM at 1 year were: LT, AIS grade, ISS, and AMS. One-year functional outcome (SCIM) can be estimated by a simple equation that takes into account four parameters of the initial physical examination. Estimating the patient long-term outcome early after traumatic SCI is important in order to define the management strategies that might diminish the costs and to give the patient and family a better view of the long-term expectations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".