Bibliographic record
Abstract
BACKGROUND: Pharmaceutical advertisements are an important means of bringing drug information to physicians. Most advertisements are intended only to raise awareness, though there are those that do seek to persuade through presentation of research findings. Researchers have questioned the quality of the research reported in advertisements and wonder whether these advertisements would lead to improper prescribing. METHODS: A consecutive 6-month sample of advertisements in 4 general medical journals, 3 from the United States and 1 from Canada, were reviewed to determine how research results are presented in pharmaceutical advertisements. RESULTS: During this time there were 187 distinctive advertisements, with 43 data presentations in the 33 advertisements that contained quantitative research results. These results were examined using a critical appraisal worksheet. References to randomization and blinding were found in less than one half of the 43 data presentations. P values were frequently provided, but confidence intervals and references to power and number needed to treat were not provided in any of the advertisements. CONCLUSIONS: Descriptions of research in pharmaceutical advertisements were brief and incomplete, and they inconsistently provided the basic design and statistical information needed to judge the results reported. More detail could make these advertisements more meaningful to critical readers.
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 | 0.028 | 0.172 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".