Diagnostic Accuracy of the Conners’ Adult ADHD Rating Scale in a Postsecondary Population
Bibliographic record
Abstract
Objective: Clinicians frequently rely upon the results of self-report rating scales when making the diagnosis of ADHD; however, little research exists regarding the ability of self-report measures to accurately differentiate ADHD from other disorders. Method: This present study investigated the ability of the Conners’ Adult ADHD Rating Scale (CAARS) to discriminate between 249 postsecondary students with carefully diagnosed ADHD and 507 clinical controls. Results: The overall discriminant validity of the CAARS was 69%, and it had an unacceptably high false positive and false negative rate. At lower prevalence rates, a high score on the CAARS has only a 22% chance of accurately identifying individuals with ADHD. Conclusion: Although the CAARS is an adequate screening measure, it should not be the main method by which a diagnosis is made, as it frequently misidentifies individuals with other psychological complaints as having ADHD. Implications for clinical practice are discussed.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".