Epidemiology of Road Traffic Crashes in Ghana from 1993 to the Second Quarter of 2004: Where Do We Go from Here?
Bibliographic record
Abstract
World Congress on Disaster and Emergency Medicine s79 awareness brought about by citizen concern during the 1980s, led to a dramatic decline in drinking and driving in the industrialized world.Based on various degrees of impairment with special reference to automobiles on public roads, it became imperative for setting maximum, allowable blood alcohol content (BAC) levels as a tool for enforcement and prevention.The most significant aspect of the BAC value is the legal limit set in each country.While almost all developed countries adhere strictly to the BAC level limits, legislation in transition countries, including Nigeria, does not incorporate legal BAC levels and their implications.Except for South Africa and Zimbabwe, no other African country seems to have any tangible BAC research or legislation.Methods: A premium digital alcohol Breathalyzer called AlcoScan CA2000 from Craig Medical was utilized to obtain BAC levels from three designated collation centers.The collation centers were the hospital, resting spot, and highways.A computer Excel package was used to analyze the findings. Results:In the hospital, 73.97% of males and 26.02% of females of the total participants were screened with different BAC levels.BAC levels >0.08% were found in 43.89% of drivers or bikers, while 56.11% had BAC levels between 0.00%-0.08%.Conclusion: The incidence of drinking and driving with specific numeric values of BAC levels was established in this study, thus confirming that several road traffic crashes were alcohol-related.Therefore, there is a need for advocacy, legislation, and sanction of impaired drivers with significant BAC levels in Nigeria.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".