Can parents believe websites’ information about methylphenidate’s side effects?
Bibliographic record
Abstract
BACKGROUND: Attention deficit and hyperactivity disorder (ADHD) is one of the most common behavioural disorders, affecting around 5% of the global population. Methylphenidate is recommended as the first-line drug treatment for ADHD for children over the age of 5 in the UK. It can have many side effects and it is important that families are well informed. Other than their healthcare professionals and friends, the major information source for families is the internet. AIMS: To evaluate the validity of online information regarding the adverse effects of methylphenidate. METHODS: Side-effects of methylphenidate hydrochloride listed in the British National Formulary for Children (BNFC) were taken as the 'gold standard' and compared with online websites for accuracy. The first 10 websites found on each of nine different search engines were used as comparators. RESULTS: From the 90 hits, 10 top hits found in each of 9 search engines, 25 unique websites were identified. A quarter (six sites; 24%) documented only side-effects that all appeared in the BNFC. Three quarters (19 websites; 76%) had at least one side-effect that did not appear in the BNFC; with six websites documenting more than five side-effects not found in the BNFC. CONCLUSIONS: Methylphenidate's frequent use makes it important that the general public are provided with accurate, reliable and easily accessible information. Most websites have dependable quality information on side effects, but several seem to list excessive side-effects.
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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 | 0.004 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".