Do Physicians/Researchers Trade Stock Based on Privileged Information? A Closer Look at Trading Patterns Surrounding the Annual ASCO Conference
Bibliographic record
Abstract
There is a concern that physicians/researchers are inappropriately profiting (by buying or selling stock) from information derived from advance copies of high-impact clinical trial data distributed by medical conferences or journals. Despite these concerns, it has never been systematically evaluated, and little is known about the degree to which it exists. This is largely due to difficulties associated with directly verifying whether or not such activities have taken place and, furthermore, many medical conferences/journals today have taken the necessary actions to guard against this. One medical association in particular, the American Society of Clinical Oncology (ASCO responsible for conducting the largest annual oncology-related medical conference), only began responding to such concerns several years ago. Their actions during that time serve as a compelling case study, with wide-ranging ramifications, and provide the unique opportunity to delve into this phenomenon. Up until 2008, ASCO selectively and discreetly distributed abstracts from all forthcoming presentations (at the ASCO Conference) to ASCO members two weeks prior to it becoming publicly accessible during the conference.
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 | 0.016 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".