Theranos: Almost Complete Absence of Laboratory Medicine Input
Bibliographic record
Abstract
The biotechnology company Theranos has been consistently appearing across newspaper and magazine headlines over the past few years (1–3). In 2014, the firm's then 30-year-old founder, charismatic Stanford-dropout Elizabeth Holmes, began to receive tremendous amounts of media attention (1–4). She was trumpeted as a visionary as she proclaimed her plan to revolutionize blood testing (2). Her company garnered hundreds of millions of dollars following her claim that Theranos had designed a system to run hundreds of tests on a miniscule amount of blood drawn via finger prick (3). The company, partnered with Walgreens (a major American drug store chain), began opening Theranos Wellness Centers where individuals could order their own tests and seemed poised to completely disrupt the traditional blood testing industry (2). However, in October 2015, Wall Street Journal investigative journalist John Carreyrou began to uncover the truth behind Theranos's claims (5). In a series of shattering revelations, the public learned that not only was Theranos's technology weak but also that Holmes and her …
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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 | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".