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Enregistrement W2891655883 · doi:10.1373/jalm.2018.027474

Theranos: Almost Complete Absence of Laboratory Medicine Input

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

Notice bibliographique

RevueThe Journal of Applied Laboratory Medicine · 2018
Typeeditorial
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Organismes subventionnairesnon disponible
Mots-clésPsychologyMedicine

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,012
score de la tête « metaresearch » (Gemma)0,041
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,988
Score d'incertitude au seuil0,062

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0120,041
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0040,004
Communication savante0,0090,007
Science ouverte0,0030,002
Intégrité de la recherche0,0170,027
Charge utile insuffisante (le modèle a refusé de juger)0,0150,011

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,056
Tête enseignante GPT0,384
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
DomaineÉvaluation
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2018
Routes d'admission1
Résumé présentoui

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