Bibliographic record
Abstract
In 2014, over 100,000 people in the United States were hospitalized because of alcohol-related traffic crashes, and 9,967 died (exceeding the 6,721 US deaths from HIV in the same year) [1,2].On 17 March 2017, the National Highway Traffic Safety Administration (NHTSA) plans to promote a safety campaign against drunk driving.Past estimates suggest that law enforcement against drunk driving reduces traffic fatalities by 20% and that high-probability detection is more effective than high-severity punishment [3,4].Yet, 12 states in the US, including the large states of Texas and Minnesota, prohibit random sobriety checkpoints, and the remaining have uneven efforts against drunk driving [5].This Perspective identifies some groups with a vested interest in preventing drunk driving, describes reasons for the relative inaction, and proposes more action by physicians.Traditionally, physicians and allied health care providers have deferred to others about how to address the health risks of drunk driving.One explanation is that drunk driving is a behavioral choice, and behavioral change is difficult to effect in a time-limited clinical encounter [6].Moreover, preventive care may provide less evident benefit to the patient than prescribing an acid blocker, for example, to treat symptomatic alcohol-induced gastritis.While a pregnant woman who drinks alcohol is likely to be warned by her obstetrician or midwife on the risks to fetal development, most patients in our experience who are prone to drunk driving are easily missed because physicians rarely ask about drunk driving, despite often asking about alcohol.As a consequence, standard care may fail to identify this prevalent, modifiable, and serious health risk.Vehicle manufacturers are the most powerful commercial group that can promote traffic safety.Over time, this industry has carefully developed and marketed technologies to protect drivers, such as seat belts, airbags, antilock brakes, and safety glass.Currently, the main technology to prevent drunk driving is an ignition interlock that forces drivers to have a breath test before engine engagement.This device, now imposed only on the vehicles of convicted drunk drivers, is unlikely to be adopted broadly any time soon unless manufacturers want to boast that they make the safest cars for those prone to drunk driving.The net result is that vehicle regulators in the US are unable to rely on manufacturer innovations or economic forces to prevent drunk driving.Other large groups have even less incentive to promote sobriety while driving.Alcohol manufacturers promote "responsible drinking," which is a vacuous tautology because adverse events can be deemed "irresponsible" by rhetorical hindsight.Celebrities in the entertainment industry are occasionally charged with drunk driving yet rarely express enduring regret.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".