Towards Development of a Survival Prediction Tool for Pediatric Head Injury
Bibliographic record
Abstract
BACKGROUND: The ability to provide an accurate prognosis for children with traumatic brain injury (TBI) would be useful for the children's families and the caregivers. In this study we examined whether an appropriate mathematical model can predict survival in this patient population. METHODS: Data from the Children's Hospital of Eastern Ontario (CHEO) TBI registry was analyzed. First, a series of univariate logistic regressions was performed to ascertain the significance of individual predictors, such as age, maximum Glasgow Coma Scale (GCS) score, maximum head injury Abbreviated Injury Scores (AIS) and the Injury Severity Score (ISS). Second, a multinomial logistic regression was fitted using only individually significant predictors and inmodel predictor significance, and interactions were tested. Only two significant predictors were kept in the final model. This final model was subsequently used to predict survival for each individual patient using the n-1 training set (i.e. Lachenbruch's leave-one-out method). The receiver operating characteristics (ROC) method was used to ascertain specificity-sensitivity trade-offs at different probability cut-offs in order to predict survival. RESULTS: Only the maximum GCS and head injury AIS remained significant, both individually and in the polynomial logistic regression. Empiric ROC curve analyses from leave-one-out survival predictions showed statistical significance (area under the curve = 0.87, Z = 6.8, p < 0.001). Only 12% of cases were misclassified using the 'best' cut-off. CONCLUSION: An outcome predictive model for pediatric TBI can be devised using an appropriate mathematical model. It may help to estimate expected outcomes in pediatric TBI more objectively.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".