Use of Regression Tree Analysis for Predicting the Functional Outcome after Traumatic Spinal Cord Injury
Bibliographic record
Abstract
Predicting the long-term functional outcome after traumatic spinal cord injury (TSCI) is needed to adapt medical strategies and plan an optimized rehabilitation. This study investigates the use of regression trees for the development of predictive models based on acute clinical and demographic predictors. This prospective study was performed on 172 patients hospitalized after TSCI. Functional outcome was quantified using the Spinal Cord Independence Measure (SCIM) collected within the first-year post-injury. Age, delay before surgery, and Injury Severity Score (ISS) were considered as continuous predictors whereas energy of injury, trauma mechanisms, neurological level of injury, injury severity, occurrence of early spasticity, urinary tract infection, pressure ulcer, and pneumonia were coded as categorical inputs. A simplified model was built using only American Spinal Injury Association Impairment Scale grade, neurological level, energy, and age as predictor and was compared to a more complex model considering all 11 predictors mentioned above. The models built using 4 and 11 predictors were found to explain 51.4% and 62.3% of the variance of the SCIM total score after validation, respectively. Severity of the neurological deficit at admission was found to be the most important predictor. Other important predictors were the ISS, age, neurological level, and delay before surgery. Regression trees offer promising performances for predicting the functional outcome after a TSCI. It could help to determine the number and type of predictors leading to a prediction model of the functional outcome that can be used clinically in the future.
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".