The diagnosis of attention-deficit/hyperactivity disorder (ADHD) by psychologists, pediatricians, and general practitioners.
Bibliographic record
Abstract
It was the goal of the current study to provide a comprehensive assessment of the diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) and several factors which may relate to diagnostic accuracy in particular. Specifically, the study used four conditional probability indices---sensitivity, specificity, hit rate, and miss rate---to examine the relationship between diagnostic accuracy and the profession of the practitioner making the diagnosis, behaviour/symptom information, and gender information of a case. A mail survey method was used to present eight written case vignettes, which depicted four male and four female symptom presentations, to Ontario psychologists, pediatricians, and general practitioners for diagnosis. Symptom presentations included ADHD-Combined Type, ADHD-Predominately Hyperactive/Impulsive Type, ADHD-Predominately Inattentive Type, and a non-ADHD vignette. Data was also collected from professional groups regarding practitioner attributes and practice- and diagnosis-related variables. One hundred and twenty individuals returned completed surveys for a response rate of approximately 14%. Overall, results suggested that practitioners tended to diagnose ADHD when symptom information indicated that such a diagnosis was warranted; however they also tended to diagnose ADHD in the non-ADHD case, for which such a diagnosis was inappropriate. Misdiagnoses rates in terms of subtype ranged from 22% to 92% across presentations, with the most common misdiagnosis being ADHD-Combined Type. Results also indicated that diagnostic accuracy, as defined by various conditional probability indices, differed depending on the profession of the practitioner making the diagnosis, behaviour/symptom information, and gender information of the case. Results are discussed with respect to differences between professional groups as well as possible limitations of the current classification system. Source: Dissertation Abstracts International, Volume: 63-04, Section: B, page: 2081. Adviser: R. Orr. Thesis (Ph.D.)--University of Windsor (Canada), 2001.
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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 | 0.009 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".