Decision tree analysis to better control treatment effects in spinal cord injury clinical research
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to use decision tree modeling to identify optimal stratification groups considering both the neurological impairment and spinal column injury and to investigate the change in motor score as an example of a practical application. Inherent heterogeneity in spinal cord injury (SCI) introduces variation in natural recovery, compromising the ability to identify true treatment effects in clinical research. Optimized stratification factors to create homogeneous groups of participants would improve accurate identification of true treatment effects. METHODS: The analysis cohort consisted of patients with acute traumatic SCI registered in the Vancouver Rick Hansen Spinal Cord Injury Registry (RHSCIR) between 2004 and 2014. Severity of neurological injury (American Spinal Injury Association Impairment Scale [AIS grades A-D]), level of injury (cervical, thoracic), and total motor score (TMS) were assessed using the International Standards for Neurological Classification of Spinal Cord Injury examination; morphological injury to the spinal column assessed using the AOSpine classification (AOSC types A-C, C most severe) and age were also included. Decision trees were used to determine the most homogeneous groupings of participants based on TMS at admission and discharge from in-hospital care. RESULTS: The analysis cohort included 806 participants; 79.3% were male, and the mean age was 46.7 ± 19.9 years. Distribution of severity of neurological injury at admission was AIS grade A in 40.0% of patients, grade B in 11.3%, grade C in 18.9%, and grade D in 29.9%. The level of injury was cervical in 68.7% of patients and thoracolumbar in 31.3%. An AOSC type A injury was found in 33.1% of patients, type B in 25.6%, and type C in 37.8%. Decision tree analysis identified 6 optimal stratification groups for assessing TMS: 1) AOSC type A or B, cervical injury, and age ≤ 32 years; 2) AOSC type A or B, cervical injury, and age > 32-53 years; 3) AOSC type A or B, cervical injury, and age > 53 years; 4) AOSC type A or B and thoracic injury; 5) AOSC type C and cervical injury; and 6) AOSC type C and thoracic injury. CONCLUSIONS: Appropriate stratification factors are fundamental to accurately identify treatment effects. Inclusion of AOSC type improves stratification, and use of the 6 stratification groups could minimize confounding effects of variable neurological recovery so that effective treatments can be identified.
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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 | 0.103 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".