Enhancing the Standardized Field Sobriety Test to detect cannabis impairment: An observational study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to explore various tests of impairment that could potentially be added to the Standardized Field Sobriety Test (SFST) to enhance its sensitivity to identify drivers whose abilities are adversely affected by cannabis. METHODS: An observational study was conducted in which participants were invited to use their own cannabis at the research facility. Once prior to cannabis use and at four times during the 150 min after cannabis use, participants performed the three tests of the Standardized Field Sobriety Test (SFST) (i.e., Horizontal Gaze Nystagmus, Walk and Turn, and One Leg Stand) as well as the Modified Romberg Balance and Finger to Nose tests. In addition, assessments were made of physiological indicators (i.e., eyelid, leg and body tremors, rebound dilation, lack of convergence) and vital signs (pulse, blood pressure and body temperature). Participants also completed a digit-symbol substitution task at each testing interval. With the exception of vital signs and the digit symbol task, all tests and assessments were administered and scored by certified Drug Recognition Experts using the standard procedures of the Drug Evaluation and Classification Program. RESULTS: Twenty minutes after vaping cannabis (mean THC concentration = 6.34 ng/mL), participants displayed performance deficits on a variety of tasks; 67% met the criterion for suspected impairment on the SFST. Addition of the Finger-to-Nose (FTN) test along with observations of head movements and jerks (HMJ) increased the percentage of participants who met the criterion for suspected impairment by 33% and improved the sensitivity of the test from 0.67 to 0.88. CONCLUSIONS: The results of this study support supplementing the SFST with the Finger-to-Nose test and observations of HMJ to assist in the detection of drivers who are adversely affected by the use of cannabis. The observational study design and the use of assessors who were not blinded as to the use of cannabis by participants limits the strength of the evidence. Further research, including randomized trials and field studies of drivers, is required to confirm and validate this enhanced version of the SFST.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".