How big is the medical writing industry? Why it matters
Bibliographic record
Abstract
Medical writing is a key element in pharmaceutical companies' efforts to shape the relevant medical science literature. As part of what is called 'publication planning', medical writing can influence the knowledge base on which prescribers make decisions, and can build specific claims in targeted sales efforts. Most publication planning is done by hired medical education and communication companies (MECCs), with the rest done by other commercial entities, such as units of pharmaceutical companies or of contract research organizations, that provide essentially the same services as MECCs. Here we provide an estimate of the number of MECCs and comparable entities contributing to the medical science literature in English. To identify these companies, we collected data from Web of Science (858 named firms from 20 498 papers mentioning medical writing assistance), LinkedIn (410 company profiles), and Google and DuckDuckGo (68 company websites). After removing duplicates and false positives, we found 1148 MECCs and other comparable entities providing medical writing services. More than 50% of Web of Science papers that acknowledged medical writing support are sponsored by only ten pharmaceutical companies. Most of the remaining papers in our database are sponsored by other pharmaceutical, device, and biotechnology companies. This study likely undercounts MECCs, because it depends on some level of transparency in publications or other leakage of information. Our combining multiple sources for the data should limit the undercount of MECCs. The study does not identify MECCs that work exclusively in languages other than English.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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; a candidate call from one teacher head, 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".