A Novel Method to Classify Cervical Incomplete Spinal Cord Injury Based on Potential for Recovery: A Group-Based Trajectory Analysis
Bibliographic record
Abstract
The outcomes of cervical incomplete spinal cord injury (SCI) are heterogeneous. This study sought to dissociate subgroups of cervical incomplete SCI patients with distinct longitudinal temporal profiles of recovery in upper limb motor function. Patients with cervical incomplete SCI (American Spinal Injury Association Impairment Scale [AIS] B-D; C1-C8) were identified from four prospective, multi-center SCI datasets. A group-based trajectory model was fit to longitudinal upper extremity motor scores out to 1 year. Multi-variable multinomial logistic regression was performed to identify features that characterize each trajectory group. A classification system for predicting trajectory group at baseline was developed by recursive partitioning. In total, 801 patients were eligible. Four distinct trajectory groups were identified: 1) “Poor outcome”: Severe injury, very minimal recovery; 2) “Moderate recovery”: Moderate-to-severe injury, moderate recovery; most recovery occurs by 6 months, with mild, gradual recovery continuing thereafter; 3) “Good recovery”: Moderate injury, good recovery; most recovery occurs by 3 months, with mild, gradual recovery continuing thereafter; and 4) “Excellent outcome”: Mild injury, recovery to normal/near-normal by 3 months. On adjusted analyses, older age was associated with lower likelihood of “excellent outcome” ( p = 0.020). AIS C and D injuries were associated with “moderate recovery,” “good recovery,” and “excellent outcome” ( p < 0.001). Mid-cervical injuries occurred more frequently in “moderate recovery,” “good recovery,” and “excellent outcome” ( p < 0.001) groups. Early surgical decompression (< 24 h) was associated with increased propensity for “good recovery” ( p = 0.039) and “excellent outcome” ( p = 0.048). A classification model based on recursive partitioning could predict trajectory group using age, AIS grade, and neurological level with an area under the curve of 0.81. Patients with cervical incomplete SCI demonstrate distinct temporal profiles of recovery in upper limb motor function. The trajectory a patient is likely to follow may be predicted at baseline with fair accuracy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".