A Comparison of KABCO and AIS Injury Severity Metrics Using CODES Linked Data
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: The research objective is to compare the consistency of distributions between crash assigned (KABCO) and hospital assigned (Abbreviated Injury Scale, AIS) injury severity scoring systems for 2 states. The hypothesis is that AIS scores will be more consistent between the 2 studied states (Maryland and Utah) than KABCO. METHODS: The analysis involved Crash Outcome Data Evaluation System (CODES) data from 2 states, Maryland and Utah, for years 2006-2008. Crash report and hospital inpatient data were linked probabilistically and International Classification of Diseases (CMS 2013) codes from hospital records were translated into AIS codes. KABCO scores from police crash reports were compared to those AIS scores within and between the 2 study states. RESULTS: Maryland appears to have the more severe crash report KABCO scoring for injured crash participants, with close to 50 percent of all injured persons being coded as a level B or worse, and Utah observes approximately 40 percent in this group. When analyzing AIS scores, some fluctuation was seen within states over time, but the distribution of MAIS is much more comparable between states. Maryland had approximately 85 percent of hospitalized injured cases coded as MAIS = 1 or minor. In Utah this percentage was close to 80 percent for all 3 years. This is quite different from the KABCO distributions, where Maryland had a smaller percentage of cases in the lowest injury severity category as compared to Utah. CONCLUSIONS: This analysis examines the distribution of 2 injury severity metrics different in both design and collection and found that both classifications are consistent within each state from 2006 to 2008. However, the distribution of both KABCO and Maximum Abbreviated Injury Scale (MAIS) varies between the states. MAIS was found to be more consistent between states than KABCO.
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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