Bibliographic record
Abstract
OBJECTIVE: To review and comment on the long-acting medications presently marketed in Canada for the treatment of Attention Deficit Hyperactivity Disorder (ADHD) in terms of design, composition, mode of action and efficacy including other long-acting products that are not yet available in Canada. METHOD: A literature review was conducted using MEDLINE, PsycInfo, CINAHL, and PubMed with additional information gathered from other sources. RESULTS: The American Academy of Pediatrics (AAP), the American Academy of Child and Adolescent Psychiatry (AACAP) and the Canadian Attention Deficit Hyperactivity Disorder Resource Alliance (CADDRA) while endorsing the stimulants as first line medications to treat ADHD also recommended the use of long-acting once-a-day medication for better efficacy, convenience and adherence. Most studies rated the controlled release and the immediate release medications as similar in efficacy. However, long-acting medication was shown to be superior in terms of remission rates. CONCLUSION: When a child is receiving a long-acting medication for treatment of ADHD, he may feel less stigmatized, is more likely to be adherent and achieve remission. A child in remission can benefit from other treatment modalities thus improving long-term prognosis.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".