A Multiple Imputation Model for Imputing Missing Physiologic Data in the National Trauma Data Bank
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Bibliographic record
Abstract
BACKGROUND: Like most trauma registries, the National Trauma Data Bank (NTDB) is limited by the problem of missing physiologic data. Multiple imputation (MI) has been proposed to simulate missing Glasgow Coma Scale (GCS) scores, respiratory rate (RR), and systolic blood pressure (SBP). The aim of this study was to develop an MI model for missing physiologic data in the NTDB and to provide guidelines for its implementation. STUDY DESIGN: The NTDB 7.0 was restricted to patients admitted in 2005 with at least one anatomic injury code. A series of auxiliary variables thought to offer information for the imputation process was selected from the NTDB by literature review and expert opinion. The relation of these variables to physiologic variables and to the fact that they were missing was examined using logistic regression. The MI model included all auxiliary variables that had a statistically significant association with physiologic variables or with the fact that they were missing (Bonferroni-corrected p value <0.05). RESULTS: The NTDB sample included 373,243 observations. Glasgow Coma Scale, respiratory rate, and systolic blood pressure were missing for 20.3%, 3.9%, and 8.5% of data observations, respectively. The MI model included information on the following: gender, age, anatomic injury severity, transfer status, injury mechanism, intubation status, alcohol and drug test results, emergency department disposition, total length of stay, ICU length of stay, duration of mechanical ventilation, and discharge disposition. The MI model offered good discrimination for predicting the value of physiologic variables and the fact that they were missing (areas under the receiver operating characteristic curve between 0.832 and 0.999). CONCLUSIONS: This article proposes an MI model for imputing missing physiologic data in the NTDB and provides guidelines to facilitate its use. Implementation of the model should improve the quality of research involving the NTDB. The methodology can also be adapted to other trauma registries.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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