Statistical Validation of the Glasgow Coma Score
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To validate the predictive value of the Glasgow Coma Score (GCS) and find the best way to model the score in a logistic regression model predicting mortality. METHODS: Analyses were based on 20,494 patients from the trauma registries of three urban Level I trauma centers in the province of Quebec, Canada. The predictive value of the GCS and its components was evaluated in logistic regression models predicting in-hospital mortality with measures of discrimination and calibration. The performance of the GCS with no transformation and as an ordered categorical variable was compared with two transformation techniques: fractional polynomials and spline regression. RESULTS: The GCS had excellent discrimination (area under Receiving Operator Characteristic Curve=0.833 95% confidence interval=0.820-0.846) but fairly poor calibration (Pearson's Chi-squared statistic=122 on 11 df). The eye component added no predictive information to the verbal and motor components in the whole sample but was important in certain sub-populations. Using the three components separately, rather than the sum, did not improve the predictive model. Fractional polynomial transformation of the GCS improved calibration and spline regression performed even better. GCS modeled as an ordered categorical variable performed badly both in terms of discrimination and calibration. CONCLUSIONS: The GCS in its present form is an efficient predictor of in-hospital mortality, which could benefit from statistical transformation in logistic regression models when the accuracy of estimated probabilities of mortality is important. The common use of GCS categories for modeling mortality leads to loss of information and should be discarded.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it