Governance basics for the physician-scientist considering business ventures. Lessons from Theranos
Bibliographic record
Abstract
Abstract The prospect of an innovative laboratory device capable of an array of testing from a tiny amount of blood caught the intense attention of both the medical and investment community. The device, however, was never properly validated, with several false and misleading claims made by its founder. This venture in the business of science went very badly for the firm Theranos with ensuing criminal convictions. Using public domain reports from trial testimony provided a unique opportunity to distill facts for key learnings for future stakeholders in the business of science. Several lessons related to basic governance unfolded during the trial’s testimony and are the basis for this brief case study. These include (1) a board make-up that had a suboptimal understanding of the technology, (2) advisors that did not sufficiently engage, (3) management/employee trust was tarnished and (4) investors failing to perform optimal diligence prior to funding. These lessons are particularly important for the physician-scientist and health executive who may find themselves at the interface of health and commerce. Points to consider in such ventures are discussed toward fostering the avoidance of these breakdowns.
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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.012 | 0.013 |
| 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".