6.9 Two symptoms to triage acute concussions: using decision tree modeling to predict prolonged recovery following concussion
Bibliographic record
Abstract
Objective The objective was to examine the 22 variables from the Sport Concussion Assessment Tool’s 5th Edition (SCAT5) Symptom Evaluation using a decision tree analysis to identify those most likely to predict prolonged recovery following sport-related concussion. Design Cross sectional. Setting Single site – Primary care. Participants 273 patients (52% male, mean age 21 ± 7.6 years) initially assessed by either an emergency medicine or sport medicine physician within 14 days of concussion (mean 6 ± 4 days). Interventions (or Assessment of Risk Factors) 22 symptoms from the Sport Concussion Assessment Tool 5th Edition Outcome Measures A decision tree analysis was performed using RStudio and the R package rpart. The decision tree was generated using a complexity parameter of 0.045, post-hoc pruning was conducted with rpart and the package carat was used to assess the final decision tree’s accuracy, sensitivity and specificity. Main Results Of the 22 variables, only 2 contributed towards the predictive splits: Feeling like ‘in a fog’, and Sadness. The confusion matrix yielded a statistically significant accuracy of 0.7636 (p-Value [Acc > NIR]: 0.00009678), sensitivity of 0.6429, specificity of 0.8889, positive predictive value of 0.8571 and a negative predictive value of 0.7059. Conclusions Decision tree analysis yielded a statistically significant decision tree model which can be used clinically to identify patients at initial presentation who are at a higher risk of having prolonged symptoms lasting 28 days or more post-concussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".