Bibliographic record
Abstract
OBJECTIVE: To review the ways attention deficit disorder (ADD) presents in adults in primary care and to suggest treatment approaches. SOURCES OF INFORMATION: PsycINFO, PubMed, and Academic Search Elite databases were searched. Level I evidence supports the effectiveness of stimulants for treating ADD in adults, and mixed evidence (levels I and II) supports the effectiveness of antidepressants. MAIN MESSAGE: Attention deficit disorder is a prevalent but often unrecognized disorder in adults. The diagnosis, which must include onset of symptoms before age 7, is often missed. This could be because family physicians are not always familiar with the presentation in adults, because it frequently presents with comorbid problems, or because specific questions are not asked to elicit the diagnosis. Diagnosis is based on clinical assessment often assisted by self-rating scales. Management includes support and education, helping patients develop additional structure in their lives and make necessary behavioural changes, enhancing self-esteem, supporting and educating families, and prescribing medication. Medication choices include stimulants and antidepressants; medication can benefit up to 60% of people with ADD. CONCLUSION: It is crucial for primary care physicians to identify ADD in adults and to initiate treatment or referral. Several simple interventions can be employed.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".