Gender differences and age of diagnosis in ADHD
Bibliographic record
Abstract
Introduction Female patients tend to be more often diagnosed with ADHD inattentive subtype. Many of these females deny an earlier childhood history of hyperactivity or disruptive behaviours and hence may have been clinically overlooked in childhood, while their male counterparts may have presented with ADHD combined subtype which tends to be more easily identified and diagnosed. Objectives Participants will learn about gender differences in ADHD diagnosis and epidemiology. Aims To examine gender differences in the age of first diagnosis of ADHD in a clinical population. Methods The study sample consisted of 118 males and 61 females (ages 17–69). Each patient was evaluated and diagnosed by an experienced psychiatrist. Clinical measures (administered by a trained psychometrist) included the CAARS (self and observer versions), BRIEF (self and informant versions), CAADID (history and structured interview), ASRS, CPT, Beck Anxiety and Beck Depression Inventories. Patients were referred by family physicians to a large out-patient metropolitan psychiatry program specializing in ADHD. Information regarding childhood diagnosis was collected retrospectively during the clinical interview. Results In this study the mean age of diagnosis (ADHD) for males was 31.2 years versus 32.1 for females. Neither t-test (p = 0.44) nor non-parametric testing using Mann-Whitney U Test (p = 0.67) showed any statistical differences between the two groups. Conclusions In the present study there were no difference in the mean age of first diagnosis between male and female subjects. In the present study Further studies are needed to clarify this question. Selection factor may have been a factor in these results.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".