Determining cutoff scores on the Conners’ adult ADHD rating scales that can definitively rule out the presence of ADHD in a clinical sample
Bibliographic record
Abstract
In recent years, the prevalence of Attention Deficit/Hyperactivity Disorder (ADHD) and the number of individuals seeking ADHD assessments has risen significantly, leading to an increased demand for accurate diagnostic tools. This study aimed to identify cutoff scores on the Conners' Adult ADHD Rating Scales (CAARS-S:L) that can definitively rule out the presence of ADHD. Among 102 clinically diagnosed adult ADHD participants and 448 non-ADHD participants who completed the CAARS-S:L, a receiver operating characteristic curve analysis established a perfectly discriminant cutoff T-score of <44 on the ADHD Symptoms Total subscale when looking at any ADHD diagnosis and <54 on the Inattentive Symptoms subscale when looking at individuals diagnosed with the inattentive subtype of ADHD. Alternative cutoffs of <54 (ADHD Symptoms Total subscale) and <63 (Inattentive Symptoms subscale) were also identified, both with a sensitivity of 0.95 or higher. Furthermore, the analysis found the ADHD Index to be a poor predictor of a negative ADHD diagnosis, suggesting against the use of this scale for cutoff determination. Despite this limitation, these findings indicate that with specific cutoffs, the CAARS-S:L may have the potential to conclusively rule out ADHD, effectively streamlining the diagnostic process and reducing unnecessary comprehensive assessments in clear negative cases.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".