Public health system expenditure on motor vehicle collisions in Brazil: an ecological study
Bibliographic record
Abstract
PURPOSE: To assess the cost of traffic accidents in Brazil and the impact of age/location. METHODS: All patients admitted to a Brazilian hospital due to traffic accidents from January 2012 to December 2022 and cost of hospital services were obtained from the Department of Information Technology of the Unified Health System. Demographic data were collected in the Brazilian Institute of Geography and Statistics database. Parametric and nonparametric data were analyzed. The Kruskal-Wallis' test and a post-hoc test were used for data comparison. The ARIMA linear regression method for trend estimation. RESULTS: In Brazil, 1.6 million individuals were involved in traffic accidents between 2012-2022, resulting in a cumulative hospital expenditure of US$ 38 million. The average hospital admission cost during this time was US$ 239.66, but no correlation was found between the cost per capita and driver population density increase. Hospitalization in the Midwest/South was higher. CONCLUSION: The economic impact of traffic accidents on the Brazilian public health system is significant. With a high number of victims admitted annually and evident regional and age-related disparities, there is a clear need for comprehensive and cost-effective healthcare strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".