Clothing-related motorised two-wheeler crashes: results from a traffic injury surveillance study, Karachi, Pakistan
Bibliographic record
Abstract
Introduction Recently, cases of clothing-related entanglement in motorcycles have been increasingly reported from India and Pakistan but no epidemiological studies are available for such crashes. Objective To assess the injury burden and severity of clothing-related two-wheeler crashes in Pakistan. Methods The study setting was Karachi, Pakistan. Cases were defined as riders and pillion riders of motorised two-wheelers involved in a crash as a result of entangling of clothing in parts of the vehicle. Cases were selected from an ongoing traffic injury surveillance study. For each case, Abbreviated Injury Scale (AIS) of involved body parts was recorded and used in computation of New Injury Severity Score (NISS). Results A total of 986 injuries were reported from January 2007 to December 2009. Most were females (73.9%) and pillion riders (80.6%). These crashes were mostly single vehicle (98.5%) and involved head (41.5%), face (35.9%), limbs (51.0%), and external body that is, skin (60.3%). As per NISS, one-third of injuries were moderate (26.7%) to severe (10.2%). A total of 10 deaths were reported and head and face injuries were reported in nine of them. Female gender, age≥45 years (21.5%), pillion riders (80.6%) and crashes occurring at intersections (34.3%) were more likely to result in severe NISS than other users (p<0.001). Significance Clothing-related motorised two-wheeler injuries are common in Karachi indicating the risks associated with wearing traditional clothing when riding motorcycles. Promotion of appropriate, conspicuous clothing by both riders and pillion riders might prevent such injuries.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".