Bibliographic record
Abstract
Objective:To investigate the epidemiological characteristics of emergency traffic injury.Method:2079 cases of patients of traffic injury from January 2000 to December 2008 were analyzed by age,season,type of trauma,traumatic causes of death,mortality and other classifications retrospectively.Result:1.3% patients were less than 14 years old,939 cases were from 14 to 39 years old(45.2%),865 cases were 40 to 59 years old(41.6%),176 cases were 60 to 79 years old(8.5%),71 cases were more than 80 years old(3.4%).Quarterly basis statistics showed that 383 cases(18.4%)happened in the first quarter,555 cases(26.7%)in the second quarter,619 cases(29.8%)in the third quarter,522 cases(25.1%)in the fourth quarter.The number of traffic injuries in the third quarter was the largest.The number of traffic injury cases in a variety of trauma cases accounted for 65.2%,and the proportion increased continually.The most common parts in traffic injuries were the brain damage(56.2%)and a variety of fractures(15.7%).Brain trauma(56.4%)and multiple injuries(33.7%)were the most common cause of death in traffic injuries.Conclusion:The epidemiological analysis of traffic injuries and the formulation of scientific preventive measures to improve the emergency systems and services and the strengthening of emergency trauma care training of professionals can improve the success rate of resuscitation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".