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Record W3001214788 · doi:10.1136/bmj.l6925

Industry funding of patient and health consumer organisations: systematic review with meta-analysis

2020· review· en· W3001214788 on OpenAlexaff
Alice Fabbri, Lisa Parker, Cinzia Colombo, Paola Mosconi, Giussy Barbàra, Maria Pina Frattaruolo, Edith Lau, Cynthia M. Kroeger, Carole Lunny, Douglas M Salzwedel, Barbara Mintzes

Bibliographic record

VenueBMJ · 2020
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsCochraneUniversity of British Columbia
Fundersnot available
KeywordsMeta-analysisBusinessSystematic reviewMEDLINEMarketingMedicinePolitical scienceInternal medicineLaw

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate pharmaceutical or medical device industry funding of patient groups. DESIGN: Systematic review with meta-analysis. DATA SOURCES: Ovid Medline, Embase, Web of Science, Scopus, and Google Scholar from inception to January 2018; reference lists of eligible studies and experts in the field. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Observational studies including cross sectional, cohort, case-control, interrupted time series, and before-after studies of patient groups reporting at least one of the following outcomes: prevalence of industry funding; proportion of industry funded patient groups that disclosed information about this funding; and association between industry funding and organisational positions on health and policy issues. Studies were included irrespective of language or publication type. REVIEW METHODS: Reviewers carried out duplicate independent data extraction and assessment of study quality. An amended version of the checklist for prevalence studies developed by the Joanna Briggs Institute was used to assess study quality. A DerSimonian-Laird estimate of single proportions with Freeman-Tukey arcsine transformation was used for meta-analyses of prevalence. GRADE (Grading of Recommendations Assessment, Development, and Evaluation) was used to assess the quality of the evidence for each outcome. RESULTS: 26 cross sectional studies met the inclusion criteria. Of these, 15 studies estimated the prevalence of industry funding, which ranged from 20% (12/61) to 83% (86/104). Among patient organisations that received industry funding, 27% (175/642; 95% confidence interval 24% to 31%) disclosed this information on their websites. In submissions to consultations, two studies showed very different disclosure rates (0% and 91%), which appeared to reflect differences in the relevant government agency's disclosure requirements. Prevalence estimates of organisational policies that govern corporate sponsorship ranged from 2% (2/125) to 64% (175/274). Four studies analysed the relationship between industry funding and organisational positions on a range of highly controversial issues. Industry funded groups generally supported sponsors' interests. CONCLUSION: In general, industry funding of patient groups seems to be common, with prevalence estimates ranging from 20% to 83%. Few patient groups have policies that govern corporate sponsorship. Transparency about corporate funding is also inadequate. Among the few studies that examined associations between industry funding and organisational positions, industry funded groups tended to have positions favourable to the sponsor. Patient groups have an important role in advocacy, education, and research, therefore strategies are needed to prevent biases that could favour the interests of sponsors above those of the public. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42017079265.

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 imitation

Not 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.

metaresearch head score (Codex)0.059
metaresearch head score (Gemma)0.172
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.312

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.172
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.032
Bibliometrics0.0140.016
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.777
GPT teacher head0.631
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
DomainIncentives
GenreReview

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".

Quick stats

Citations73
Published2020
Admission routes1
Has abstractyes

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