± HEALTH Objective To evaluate the accuracy, completeness, clarity, source transparency, and readability of leading AI…+ HEALTH Background Early prediction of in-hospital mortality in critically ill patients can aid clinicians in optimiz…+ HEALTH Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition a…+ SCIENCE Fire ants (Solenopsis spp. Westwood) pose a major ecological and economic threat, mainly due to the invasive…± HEALTH Objectives The current study aimed to quantify the diagnostic accuracy of commonly utilized chatbots includin…+ HEALTH Objective Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools…± HEALTH Timely and accurate diagnosis of ST-elevation myocardial infarction (STEMI) is critical in military operation…+ HEALTH Colorectal cancer (CRC) exhibits substantial metabolic heterogeneity. This study developed a robust prognosti…
TruaceTracing the truth around AIWednesday, August 5, 2026
Subscribe
TRV-2026-0633Certified recordPeer-reviewed

Establishing diagnostic thresholds for adult ADHD screening in taxi drivers: validation of the CAARS-S via Item Response Theory and Machine Learning

Background Adult Attention-Deficit/Hyperactivity Disorder (ADHD) is under-recognized in professional drivers, yet it poses significant safety risks. The Conners' Adult ADHD Rating Scale Short Version (CAARS-S:SV) lacks empirically validated cutoff scores for occupational screening in Iran. This study aimed to assess the psychometric properties of the Persian CAARS-S:SV in taxi drivers and to determine optimal ADHD diagnostic thresholds using both traditional and machine-learning methods. Methods A sample of 298…

Health · The Trace — both readings · certified 2026-08-03 · v1 · article view · machine-readable

Current reading — gain

In 298 Iranian male taxi drivers, ROC and Random Forest analysis of the Persian CAARS-S:SV identified total-score cutoffs with 88% sensitivity and 86.7% specificity for adult ADHD screening.

Current reading — problem

Item Response Theory produced an unacceptably low 68% sensitivity for the total score at threshold 24, and logistic regression yielded only 16%-60% sensitivity for ADHD status in the same driver sample.

What this doesn’t fix

Findings limited to male Iranian taxi drivers and IRT total-score threshold failed due to violated assumptions, with logistic regression also performing poorly.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0633, v1: “Establishing diagnostic thresholds for adult ADHD screening in taxi drivers: validation of the CAARS-S via Item Response Theory and Machine Learning.” Truvace, 2026-08-03. /record/TRV-2026-0633 (accessed at citation time). sha256 fa58adfcd5a9b6c4…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv1fa58adfcd5a9…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0633 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.