Bibliographic record
Abstract
Traffic accidents are the most prevalent cause of death in developed countries between the ages of 1-33 years. In spite of a low motorization level in Israel, the rate of injury per 100,000 residents in Israel (2.8) was higher than in the US (1.8), NZ (1.7), Canada (1.7), Japan (1.3) and most European countries. The worst injuries were among pedestrians; particularly children aged 1-9 years and elderly (70+ years). In the past decade there have been significant advances in trauma care in Israel. Major strides included the foundation of trauma centers in hospitals, the establishment of the National Council for Trauma and the National Center for Trauma and Emergency Medicine Research at the Gertner Institute that coordinates the national trauma registry. One of the primary aims of the registry was to provide data to support decision-makers in setting national policy for accident prevention. The Israeli Police Department provides data on traffic accident victims to the Israeli Central Bureau of Statistics (CBS) which publishes the national figures. In their article in this edition of the journal, Dr Peleg and Dr. Aharonson-Daniel present a grave concern regarding the fact that details of over 50% of hospitalized traffic accident victims were not reported to the CBS by the police, including data on the severely injured casualties. Traffic accidents are a major cause of loss of life and disability, creating a heavy economic burden on the state and the health care system. Hence, the authors recommend establishing a national database which will combine data from medical and other sources and present the complete comprehensive picture of traffic accident injuries. Such a database will improve the decision-making process, providing more focused data to enhance the preparation and dissemination of appropriate injury prevention policies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.025 |
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".