A Clinical Trial Demonstration of a Web-Based Test for Alcohol and Drug Effects
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine the feasibility of a Web-installed test of skills essential to driving: target detection and divided attention. The Attention Assessment (TAA) was designed for use in global clinical trials to document the effects of alcohol and other drugs. METHOD: Scoring algorithms and data-storage tools were installed on servers in bicoastal U.S. locations. IBM PC-compatible test units with encrypted Web access and 19-inch monitors were installed at a Canadian site. A single-center, crossover design was used to compare the pharmacodynamic properties of a pharmaceutical compound under development with those of alcohol (blood alcohol concentration [BAC]=.10%) over time. For this study, 33 subjects completed four 36-hour testing periods. Blood samples and pharmacodynamic assessments were performed at 0, 1, 3, 5, 7, 9, 12, and 24 hours. Analysis of covariance was conducted on six composite TAA scores as change from baseline. RESULTS: Five of the six composite scores showed significant ethanol effects (p<.02) over a BAC range of. 1% to .05%. Within-session test-retest reliability was r=.86 and between periods was r=.51 (between Periods 1 and 2), .83 (between Periods 2 and 3), and .81 (between Periods 3 and 4). Individual impairment was evident at .05%. CONCLUSIONS: It was possible to conduct sensitive alcohol/other drug testing from a central database with secure scoring. Test installation, data monitoring, and norms assembly were performed at a remote location. TAA gives researchers the ability to immediately and normatively evaluate alcohol and drug effects in diverse global locations. Secondary applications include clinical or worksite testing. The data show improved precision over previous test versions to map the effect of drugs on visual/cognitive skills involved in driving.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".