The meteoric rise and dramatic fall of Theranos: lessons learned for the diagnostic industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it