Anthropometric characteristics and traffic accident circumstances of patients with isolated whiplash injury in University Clinical Hospital Mostar
Bibliographic record
Abstract
Aim To investigate anthropometric characteristics and traffic accident circumstances of subjects with isolated whiplash injury. Methods This cross sectional study involved 75 subjects from traffic accidents with isolated whiplash injury classified by Quebec Task Force (QTF). Anthropometric data were collected as well as claims about circumstances of traffic accidents. Results Distribution of 1st (28; 37.3%), 2nd (25; 33.3%) and 3rd (22; 29.3%) grade of whiplash injury was almost equal. Females had smaller anthropometric measurements than males; neck circumference was the most significant difference between males and females in the context of whiplash injury. The most frequent collision mechanism was impact to front (26; 34.7%) or to rear end (26; 34.7%) of a small passenger's car. Assertions of participants were that their car damage was significant (37; 49.2%) or total (24; 32%). A total of 38 (50.7%) participants claimed that they were not wearing safety belt and 52 (69.3%) did not find themselves responsible for accident. Conclusion Driving habits of our participants facilitate incidence of whiplash injuries, especially in vulnerable groups such as women and elderly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".