A predictive model to analyze factors affecting the presence of mild whiplash-associated disorders in minor motor vehicle crashes based on the Korean In-Depth Accidents Study (KIDAS) Database
Bibliographic record
Abstract
OBJECTIVES: We aimed to analyze factors affecting the severity of mild whiplash-associated disorders (WADs) and to develop a predictive model to evaluate the presence of mild WAD in minor motor vehicle crashes (MVCs). METHODS: We used the Korean In-Depth Accident Study (KIDAS) database, which collects data from 4 regional emergency centers, to obtain data from 2011 to 2017. The Collision Deformation Classification code was obtained as vehicle's damage information, and Abbreviated Injury Scale (AIS), Maximum Abbreviated Injury Scale (MAIS), and Injury Severity Score (ISS) were used as occupant's injury information. The degree of WAD was determined using the Quebec Task Force (QTF) classification, comprised of 5 stages (QTF 0-4), depending on the occupant's pain and the physician's findings. QTF 1 was defined as mild WAD, and we used QTF 0 to define those who were uninjured. For KIDAS data between 2011 and 2016, a logistic regression model was used to identify factors affecting the occurrence of mild WAD and a predictive model was constructed. Internal validity was estimated using random bootstrapping, and external validity was evaluated by applying 2017 KIDAS data. Of the 2,629 occupants in the KIDAS database from 2011 to 2016, after applying several exclusion conditions, 459 occupants were used to develop the predictive model. The external validity of the derived predictive model was assessed using the 13 MVC occupants from the 2017 KIDAS database meeting our inclusion criteria. Among the 137 MVC occupants from the 2017 KIDAS database for analysis of the external validity of the derived predictive model, the predictive model was verified for 13 MVC occupants. RESULTS: Logistic regression analysis was used to derive a predictive model based on sex, age, body mass index, type of vehicle, belt status, seating row, crush type, and crush extent. This predictive model had an explanatory power of 65.5% to determine an actual QTF of 0 and 1 (c-statistics: 0.655). As a result of the external validity analysis of the predictive model using data from the 2017 KIDAS database (N = 13), sensitivity, specificity, and accuracy were 0.500, 0.857, and 0.692, respectively. CONCLUSIONS: Using the predictive model, the results of the external validity analysis showed low sensitivity but high specificity. This predictive model provided meaningful results, with a high success rate for determining no injury to an occupant. Given our study results, future research is needed to create a more accurate predictive model that includes relevant technical and sociological factors.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".