Expediting emergency contact for car accidents. Database will link emergency contact information with vehicle ID number.
Bibliographic record
Abstract
This article describes a voluntary, nationwide database designed to put emergency contact information for motor vehicle owners in the hands of emergency responders, with the purpose of reducing the time it takes to identify accident victims and notify their emergency contacts. The initiative is part of the Health Information Technology Standards Panel’s work on the American Health Information Community’s emergency responders and electronic health records use case, which is defining the functional components and standards that will provide first responders with pertinent health information on accident victims. The author explains the rationale behind this kind of database and briefly describes two efforts in Florida and Ohio to create the Driver’s License Emergency Contact Database. Early involvement of family or an emergency contact to advocate on behalf of an accident victim and provide additional knowledge, such as pre-existing conditions, medications, and allergies, has the potential to greatly enhance a victim’s chances of survival after an accident. The author describes the proposed database, sponsored by participating automotive manufacturers, that will link an individuals’ emergency contact information to their automobile’s vehicle identification number. When launched, the system will allow consumers purchasing or leasing a motor vehicle from an authorized dealership to voluntarily list a minimum of a name and telephone number for at least one contact person. This system could link together more than 20,000 law enforcement agencies nationwide and more than 500,000 in-vehicle police mobile data devices in the United States and Canada.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.074 |
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".