Atomoxetine in clinical practice
Bibliographic record
Abstract
The authors provide clinical suggestions for optimizing the use of atomoxetine in practice. Atomoxetine is a highly specific norepinephrine inhibitor that was developed for the treatment of attention-deficit/hyperactivity disorder (ADHD) in all age groups. In clinical trials, it has a slightly lower effect size (0.2) than stimulants, but these studies exclude patients who are stimulant nonresponders as well as patients with comorbidities that constitute a relative contraindication to stimulants, such as tics, anxiety and substance use. If these common comorbid conditions were included, the outcome of the studies might have been different. Side effects, such as nausea, vomiting and stomach ache, may be mitigated by giving the medication on a full stomach, while sedation may be mitigated by giving the medication in the evening. Time course to response for atomoxetine is slower than that of stimulants, but a positive outcome provides for more consistent coverage over a 24-h period. Atomoxetine may be combined with stimulants to optimize full-day coverage with a booster effect on symptoms during the day. No studies to date examining real-life effectiveness outcomes of atomoxetine versus stimulants have examined quality of life, functioning, impact on comorbidity and long-term persistence with medication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.026 |
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".