Attention Deficit Hyperactivity Disorder Misdiagnosis: Why Medical Evaluation Should Be a Part of ADHD Assessment
Bibliographic record
Abstract
INTRODUCTION: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that interferes with multiple aspects of daily functioning and is associated with impairments in several domains. It may affect academic, educational, vocational, social, emotional, interpersonal, and health domains, and worsen risks to health outcomes. OBJECTIVE: To identify and discuss medical conditions that commonly present with symptoms resembling ADHD. METHOD: This review is selective and not systematic. It is conducted through a focused literature search through PubMed, Google Scholar, and EMBASE. Search term included "ADHD misdiagnosis", "medical conditions with ADHD like symptoms", "ADHD AND medical problems". EXCLUSION: giftedness, high IQ, and any article that does not list medical conditions. The limits applied were the following: the work must have been published in the past 20 years, be on humans, and be in the English language. RESULTS: There are several medical conditions that can be misdiagnosed as ADHD and may show a similar presentation to ADHD, particularly with inattentive symptoms. Examples include, but are not limited to, absence seizure disorder, diabetes, thyroid dysfunction, sleep deprivation, post-concussion states, inflammatory bowel disease, iron deficiency states and anemia, and disordered breathing. CONCLUSIONS: Our review suggests that a thorough medical evaluation should be conducted prior to the diagnosis of ADHD. Allied health professionals and psychologists who diagnose ADHD should seek medical clearance from a physician prior to making the ADHD diagnosis in order to reduce misdiagnosis rates and improve patient outcomes. ADHD diagnosis should follow guidelines and be carried out under a systematic standardized approach. A full medical evaluation should be conducted to assess for medical conditions that may look like ADHD or be associated with ADHD.
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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 | 0.015 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".