Alternative outcome frameworks to model injury severity outcomes of motorcyclists colliding with other vehicles
Bibliographic record
Abstract
Lacking protection compared to drivers of other vehicles, motorcyclists accounted for most casualties and fatalities. This study explores how non-motorcycle drivers affect motorcyclists’ injury outcomes in motorcycle-vehicle collisions. The motorcycle-vehicle crashes from the United Kingdom for 2016–2020 are used to estimate two alternative logit models to account for possible unobserved heterogeneities. The models are a latent class multinomial logit with class probability functions and a random threshold-parameter generalized ordered logit. With three possible injury severity levels (fatal injury, severe injury, and minor injury), the characteristics of motorcyclist, driver, roadway, environment, vehicle, and collision are considered potential determinants. Then, the temporal instability issues are revealed through the likelihood ratio tests and out-of-sample predictions based on the two models. Showing good ρ2 values of over 0.370, the latent class model’s estimation results are leveraged to quantify the effects of the contributing factors. Moreover, the marginal effects are also calculated to reveal the existing temporal instability, while some variables reflect the temporal instability in the influence trend and degree. The critical factors increasing the risk levels are male motorcyclists, higher speed limit, older ages of motorcyclists and vehicles, fine weather, single carriageway, and head-on collision type. Overall, subtle variations in the injury severity predictions exist in alternative heterogeneity modeling approaches, suffering from the modeling mechanism of different structural frameworks in capturing the unobserved heterogeneities.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".