An unsupervised machine learning approach to predict recovery from traumatic spinal cord injury
Bibliographic record
Abstract
Abstract Background Neurological and functional recovery after traumatic spinal cord injury (SCI) is highly heterogeneous, challenging outcome predictions in rehabilitation and clinical trials. We propose k-nearest neighbour (k-NN) matching as a data-driven, interpretable solution. Methods This study used acute-phase International Standards for Neurological Classification of SCI exams to forecast 6-month recovery motor function as primary evaluation endpoint. Secondary endpoints included severity grade improvement, independent walking, and self-care ability. Different similarity metrics were explored for NN matching within 1267 patients from the European Multicenter Study about Spinal Cord Injury before validation in 411 patients from the Sygen trial. Results We obtained a population-wide root-mean-squared error (RMSE) in motor score sequence of 0.76(0.14, 2.77) and competitive functional score predictions (AUC walker =0.92, AUC self-carer =0.83). The validation cohort showed comparable results (RMSE = 0.75(0.13, 2.57), AUC walker =0.92). Prediction performance in AIS grade B and C patients (∼30%) showed the largest deviations from true recovery scores, in line with large SCI heterogeneity. Conclusions Our approach provides detailed predictions of neurological and functional recovery based on a highly interpretable unsupervised machine learning concept. The k-NN matching strategy further enables the integration of historical control data into the evaluation of clinical trials and provides a data-driven digital twin for recovery trajectory exploration.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".