Trajectory Based Classification of Recovery In Sensorimotor Complete Traumatic Cervical Spinal Cord Injury – Individual Recovery Trajectory Data
Bibliographic record
Abstract
Objective To test the hypothesis that sensorimotor complete traumatic cervical spinal cord injury is a heterogenous clinical entity comprising several subpopulations that follow fundamentally different trajectories of neurological recovery. Methods We analyzed demographic and injury data from 655 patients who were pooled from four prospective longitudinal multicenter studies. Group based trajectory modeling was applied to model neurological recovery trajectories over the initial 12-months postinjury and to identify predictors of recovery trajectories. Neurological outcomes included: Upper Extremity Motor Score, Total Motor Scores and AIS grade improvement. Results The analysis identified three distinct trajectories of neurological recovery. These clinical courses included: (1) Marginal recovery trajectory: characterized by minimal or no improvement in motor strength or change in AIS grade status (remained grade A); (2) Moderate recovery trajectory: characterized by low baseline motor scores that improved approximately 13 points; or AIS conversion of one grade point; (3) Good recovery trajectory: characterized by baseline motor scores in the upper quartile that improved to near maximum values within three months of injury. Patients following the moderate or good recovery trajectories were of younger age, had more caudally located injuries, a higher degree of preserved motor and sensory function at baseline examination and exhibited a greater extent of motor and sensory function in the zone of partial preservation. Conclusion Cervical complete SCI can be classified into one of three distinct subpopulations with fundamentally different trajectories of neurological recovery. This study defines unique clinical phenotypes based on potential for recovery, rather than baseline severity of injury alone. This approach may prove beneficial in clinical prognostication and in the design and interpretation of clinical trials in SCI.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".