Can Theranos resurrect from its ashes?
Bibliographic record
Abstract
Theranos was a biotechnology company which, in the 2010s, promised to revolutionize traditional clinical chemistry testing by using novel technology, microvolumes of blood obtained by finger pricks, and performance of tests outside traditional clinical chemistry laboratories, such as in pharmacies. Theranos did not publish any details of their technology, nor sought to evaluate it independently. Despite this, it attracted millions of dollars of investments, and at a point, its market valuation reached $9 billion. Around 2015 the Theranos business practices and technology were scrutinized and it was revealed that they misled investors, doctors, and patients by falsely claiming of using their own technology, when in fact, they were using traditional clinical chemistry analyzers. The leaders of Theranos are now serving prison time. This year it was announced that a new effort is underway, by people who are related to Theranos, to start a new company, with objectives that partially overlap with those of the old company. Here, we comment on these new developments, with the hope that the lessons learned from the past will guide the new leaders to find success while mentioning that some new developments in the field will likely pose formidable competition.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".