The Ability of Self-Report Methods to Accurately Diagnose Attention Deficit Hyperactivity Disorder: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To identify and analyze all studies validating rating scales or interview-based screeners commonly used to evaluate ADHD in adults. METHOD: A systematic literature search identified all studies providing diagnostic accuracy statistics, including sensitivity and specificity, supplemented by relevant articles or test manuals referenced in reviewed manuscripts. RESULTS: Only 20 published studies or manuals provided data regarding sensitivity and specificity when tasked with differentiating those with and without ADHD. While all screening measures have excellent ability to correctly classify non-ADHD individuals (with negative predictive values exceeding 96%), false positive rates were high. At best, positive predictive values in clinical samples reached 61%, but most fell below 20%. CONCLUSION: Clinicians cannot rely on scales alone to diagnose ADHD and must undertake more rigorous evaluation of clients who screen positive. Furthermore, relevant classification statistics must be included in publications to help clinicians make statistically defensible decisions. Otherwise, clinicians risk inappropriately diagnosing ADHD.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".