Mapping Ghost Management in Medical Research and Public Health. Poster presented at PODC2018
Bibliographic record
Abstract
This poster was presented at the Preventing Overdiagnosis Conference (PODC) 2018. Title: Mapping Ghost Management in Medical Research and Public Health Catherine Riva, Serena Tinari, Re-Check Poster + Conversation Category: Other - Developing tools to investigate and identify the stakes that may lead to overdiagnosis and too much medicine Objectives: At the Preventing Overdiagnosis Conference 2017, Marc-André Gagnon (Carlton University, Ottawa) built on the concept of ghost management in science (Sergio Sismondo) to develop a reflection on corporate capture and institutional corruption in the biopharmaceutical sector. He postulates that because of the current business model, the activity of pharmaceutical firms is more oriented towards producing influence on medical knowledge and social determinants of value, than towards producing innovative treatments. We wanted to verify the validity of this hypothesis by mapping the strategies and issues highlighted in three long-form Re-Check investigations on 3rd and 4th generation oral contraceptives, systematic breast cancer screening and HPV vaccines. Method: Search for documents as well as for scientific and mainstream publications, FoIA requests, journalistic investigative methods, Re-Check evaluation grids and mapping tools. Results: The mapping of our investigations confirms the hypothesis put forward by Gagnon. Our maps show ghost management at work in the case of these three health measures targeting healthy girls and women. Ghost management is practiced by the pharmaceutical industry, but also by other players in the health system (health authorities, regulatory authorities, research centers, NGOs) as soon as the stakes reach a critical size. Our maps illustrate how this “new model of science (…) drawing its authority from traditional academic science” (Sismondo) is an effective strategy for capturing the different levels identified by Gagnon: science, regulation, market, health professions, media, technology and civil society. Conclusions: Re-Check mapping highlighting ghost management currently at work in medical research and public health confirms Gagnon’s hypothesis (Sismondo’s concept). Such methods deserve to be developed, enriched, validated and applied by academic research. They should also become part of an evaluation grid used by journalists investigating pharmaceutical companies and health system’s players: organizations devoted “to influence ideas and social structures in a way that maximizes commercial value” (Gagnon) and strengthens their position. The more investigations and academic research document the phenomenon, the better it may enable to develop solutions and transform institutional structures and medical practices to stem this endemic corruption.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".