The meteoric rise and dramatic fall of Theranos: lessons learned for the diagnostic industry
Bibliographic record
Abstract
In this piece we discuss and reflect on the conclusion of the Theranos saga in the light of its fraud conviction. Theranos (founded in 2003 by Elizabeth Holmes) was supposed to disrupt the diagnostic testing industry by developing technology which could perform dozens of tests using a tiny amount of blood from a finger-prick. As a result, Ms. Holmes rose to fame, becoming the world's youngest female self-made billionaire and was plastered across magazine covers. However, in 2014, Theranos began to fall apart following increasingly damaging revelations regarding its lack of expertise, technology, framework, extreme secrecy and inaccurate test results. This led to the closure of two of its laboratories, investor and patient lawsuits and the devaluation of Ms. Holmes's wealth to nothing. In March 2018, the United States Security Exchange Commission ordered Ms. Holmes to pay $500,000 to settle the charge of massive fraud and barred her from being a director of a publicly owned company for 10 years, likely concluding Theranos's endeavors. We conclude our series of articles on this topic by reflecting on the lessons the laboratory medicine community can learn from Theranos.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".