Variations in sub-national road traffic fatality trends in a low-income country
Bibliographic record
Abstract
BACKGROUND: In most low- and middle-income countries (LMICs), road traffic fatality (RTF) trends are presented in aggregated form at the national level. This practice omits important information regarding RTF risk at sub-national levels. OBJECTIVE: This ecological study assesses the extent of RTF variations at different sub-national levels in Pakistan, a low-income country. MATERIALS AND METHODS: Based on official statistics, significant variations in three RTF indicators i.e. per population, per registered vehicles, and per crash were compared by regression analyses at two sub-national levels i.e. provincially (2000-2009) and district-wise (2004). RESULTS: The national RTF counts are based on data from four provinces. From 2000 to 2009, RTF per population and per registered vehicles decreased in all provinces except Balochistan. RTF per crash in Punjab decreased from 0.61 to 0.56 (beta coefficient (β) year = -0.0082, P = <0.001), whereas in Balochistan it increased from 0.40 to 0.58 (β year = 0.0708, P = <0.001) over the same period. District-level comparisons were possible only in Punjab where RTF per crash varied from 0.25 to 2.15 and correlated (β = 0.50, P = 0.003) with RTF per population. CONCLUSIONS: Sub-national RTF surveillance is necessary in LMICs like Pakistan in order to prioritize available resources on high-risk jurisdictions such as the Balochistan province and districts of Punjab where high RTF per population and per crash exist.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".