Quality of Pharmaceutical Industry Press Releases Based on Original Research
Bibliographic record
Abstract
BACKGROUND: Press releases are a popular vehicle to disseminate health information to the lay media. While the quality of press releases issued by scientific conferences and medical journals has been questioned, no efforts to assess pharmaceutical industry press releases have been made. Therefore, we sought to systematically examine pharmaceutical company press releases about original research for measures of quality. METHODOLOGY/PRINCIPAL FINDINGS: Press releases issued by the ten top selling, international pharmaceutical companies in the year 2005 were selected for evaluation. A total of 1028 electronic press releases were issued and 235 were based on original research. More than half (59%) reported results presented at a scientific meeting. Twenty-one percent of releases were not explicit about the source of original data. While harms or adverse events were commonly cited (76%), study limitations were rarely noted (6%). Almost one-third (29%) of releases did not quantify study results. Studies presented in abstract form were subsequently published within at least 20 months in 53% of cases. CONCLUSIONS: Pharmaceutical company press releases frequently report basic study details. However, readers should be cautioned by the preliminary nature of the data and lack of identified limitations. Methods to improve the reporting and interpretation of drug company press releases are desirable to prevent misleading media coverage.
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 | 0.284 | 0.680 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.030 | 0.022 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".