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Record W2891655883 · doi:10.1373/jalm.2018.027474

Theranos: Almost Complete Absence of Laboratory Medicine Input

2018· editorial· en· W2891655883 on OpenAlexaff
Clare Fiala, Eleftherios P. Diamandis

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2018
Typeeditorial
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsPsychologyMedicine

Abstract

fetched live from OpenAlex

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 company had defrauded patients, investors, and regulatory bodies by hiding that they had no working technologies (6). Soon, the firm was overwhelmed with lawsuits, was forced to close its laboratories by the Centers for Medicare and Medicaid Services, and laid off the majority of its staff (7). In April 2018, Holmes paid a fine of $500000 to settle charges of massive fraud with the Securities and Exchange Commission. She was forced to give up control of Theranos and is barred from being an officer of any publicly owned company for 10 years (6). Other litigation is still ongoing, and Theranos's ex-president Ramesh (Sunny) Balwani as well as Elizabeth Holmes may face prison time if convicted (8). In May 2018, Theranos was back in the headlines again, with the release of John Carreyrou's book Bad Blood detailing his lengthy investigation into the company (9). In the May 17, 2018, issue of leading journal Nature, Eric Topol also profiles the company and reviews the book (10). The commentary and the book deal almost exclusively with Theranos's fraud of investors, partners, employees, and regulatory agencies. However, neither of these reports nor other recent accounts address a fundamental question: Where were the scientists and clinical chemists as the Theranos scandal was unveiled? The lack of scientific articles on Theranos is particularly obvious after a PubMed search. Using the term “Theranos,” PubMed returns about 15 relevant documents, despite the company operating for almost 15 years. Our group (Fiala and Diamandis) was the first to publicly voice concerns about Theranos in the scientific literature, writing an in-depth opinion piece published in June 2015 (11). We voiced concerns about Theranos's lack of expertise and transparency as well as the scientific feasibility and originality of the company's offerings. In 2015, John Ioannidis published an editorial in the Journal of the American Medical Association. His work focused on criticizing the secrecy of Theranos strategies but not its core technology, which was a well-kept secret (12). He published an update to this piece a year later (13). Ultimately, our group published 6 more pieces, analyzing Theranos's science independently of Carreyrou's business revelations. We systematically demonstrated that its “revolutionary” tests on minute amounts of blood were unlikely to succeed widely. Moreover, we showed Theranos's claims to disrupt traditional blood testing and empower patients were inaccurate or heavily exaggerated. We also wrote extensively on the dangers of patient self-testing and self-interpretation that could arise from Theranos's paradigm (14–18). Our work appeared mostly in the journal Clinical Chemistry and Laboratory Medicine (CCLM). CCLM is a PubMed-indexed journal published by DeGruyter since 1963 and is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine. The CCLM editors also contributed valuable editorials on the subject (19, 20). Despite leading journals such as Nature and Science covering the Theranos saga frequently, the AACC flagship journal chose to stay out. Finally, it is also important to mention that the few clinical chemists (including Eleftherios P. Diamandis) who were interviewed by the media expressed concerns about the lack of data and independent review of Theranos's technology (21). The Theranos example shows that the scientific community can play an important role in sharing our expertise to evaluate highly publicized biotechnology companies. As scientists, we spend our days analyzing and appraising data to determine its worth, accuracy, and translational value. Our expertise and experience position us to offer unique insight on scientific inventions that are proclaimed in the media. This insight is particularly important when a company is extremely secretive or its shortcoming could have ramifications for people's health, as was the case for Theranos. Surprisingly, the only validation of the Theranos technology was performed by nonlaboratorians (except one author) and was published in a respected but nonlaboratory medicine journal (22). We also believe it is important for professional associations, such as AACC, to be measured and appropriately cautious in giving controversial start-ups special platforms at international conferences without their data having undergone proper validation and clinical trial. Our position on this issue has been published elsewhere, and the subsequent facts vindicated our concerns as well as the concerns of numerous other AACC members (17). Years of education and training enable our colleagues and us to meaningfully highlight the insights gained and lessons learned through biotechnology disasters. For example, the long-running pipeline of translating laboratory medicine from the laboratory to the clinic shows that research groups with truly revolutionary products can often bounce back from difficulties arising in the business world and eventually find their way to success. This led our group to conclude that Theranos's double jeopardy was the lack of good science, in addition to its lack of honesty (18). Nonetheless, when the scientific community stayed mostly silent as the Theranos events unfolded, an important opportunity was lost for it to share its valuable concerns and expertise. Topol concludes his account about Theranos with a note that Carreyrou's book does not mention any lessons learned that could be useful for avoiding similar future disasters (10). In our latest published account on the subject we did exactly that, enumerating the lessons learned (18), as we also did earlier in our commentary on the CCLM blog to share our insights with a wider audience (23, 24). Despite the extensive work of investigative journalist Carreyrou on Theranos business troubles, scientists remained silent observers for over 10 years. The 8 Theranos-related papers indexed in PubMed from our group, along with their accompanying editorials and a few papers from others, provide the only parallel scientific perspective to the remarkable story described in this new book. As other biotechnology companies come along to fill the headlines, we hope that more scientists will share their expertise by evaluating the firms' methods and products in the scientific literature. These papers will serve as important and much-needed complements to the articles published in the media.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.988
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.041
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0040.004
Scholarly communication0.0090.007
Open science0.0030.002
Research integrity0.0170.027
Insufficient payload (model declined to judge)0.0150.011

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.

Opus teacher head0.056
GPT teacher head0.384
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreEditorial

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".

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Citations4
Published2018
Admission routes1
Has abstractyes

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