The Nature and Influence of Pharmaceutical Industry Involvement in Asthma Trials
Bibliographic record
Abstract
BACKGROUND: Pharmaceutical industry-sponsored research has been shown to be biased toward reporting positive results. Frequent industry participation in trials assessing the efficacy of inhaled corticosteroid (ICS) and long-acting beta<span style="vertical-align: sub">2<⁄span>-agonist (LABA) combination treatment makes assessing industry influence difficult and warrants an assessment of specific potential publication bias in this area. OBJECTIVE: To describe the frequency of industry involvement in ICS⁄LABA trials and explore associations among significant outcomes, type of industry involvement and type of primary outcome. METHODS: A systematic review of trials comparing ICS⁄LABA combination therapy with ICS monotherapy for asthma was conducted. Data concerning the type of industry sponsorship, primary outcome and statistical results were collected. Comparisons between type of sponsorship and significant results were analyzed using Pearson's chi squared test and relative risk. RESULTS: Of 91 included studies (median year of publication 2005 [interquartile range 1994 to 2008]), 86 (95%) reported pharmaceutical involvement. Author affiliation was reported in 49 of 86 (57%), and 19 of 86 (22%) were industry-reported trials without full publications. The remainder were published journal articles. Studies with a first or senior author affiliated with industry were 1.5 times more likely to report statistically significant results for the primary outcome compared with studies with other types of industry involvement. Pulmonary measures were 1.5 times more likely to be statistically significant than were measures of asthma control. CONCLUSIONS: The potential biases identified were consistent with other research focused on author role and industry involvement, and suggest that degree of bias may vary with type of affiliation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.028 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".