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All Publications

  • Stealth research: lack of peer-reviewed evidence from healthcare unicorns. European journal of clinical investigation Cristea, I. A., Cahan, E. M., Ioannidis, J. P. 2019: e13072


    In 2014, one of us (JPAI) wrote a viewpoint article coining the term "stealth research" for touted biomedical innovation happening outside the peer-reviewed literature in a confusing mix of "possibly brilliant ideas, aggressive corporate announcements, and mass media hype". These reflections were prompted by Theranos, a medical diagnosis start-up company; Theranos had not published any peer-reviewed papers [1] but made claims that its technology would "disrupt medicine." However, in contrast to the tech sector, in healthcare published peer-reviewed research is essential to ensure a minimum threshold of transparency, accountability, and credibility for the underlying work in the scientific community. This article is protected by copyright. All rights reserved.

    View details for PubMedID 30690709

  • Supply Chain Optimization and Waste Reduction. JAMA Cahan, E. M., Chawla, A., Shea, K. G. 2020; 323 (6): 572?73

    View details for DOI 10.1001/jama.2019.20854

    View details for PubMedID 32044938

  • Spinal Screening MRI Trends in Patients with Multiple Hereditary Exostoses: National Survey CUREUS Montgomery, B. K., Cahan, E. M., Frick, S. 2019; 11 (12)
  • Putting the data before the algorithm in big data addressing personalized healthcare NPJ DIGITAL MEDICINE Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., Rubin, D. L. 2019; 2: 78


    Technologies leveraging big data, including predictive algorithms and machine learning, are playing an increasingly important role in the delivery of healthcare. However, evidence indicates that such algorithms have the potential to worsen disparities currently intrinsic to the contemporary healthcare system, including racial biases. Blame for these deficiencies has often been placed on the algorithm-but the underlying training data bears greater responsibility for these errors, as biased outputs are inexorably produced by biased inputs. The utility, equity, and generalizability of predictive models depend on population-representative training data with robust feature sets. So while the conventional paradigm of big data is deductive in nature-clinical decision support-a future model harnesses the potential of big data for inductive reasoning. This may be conceptualized as clinical decision questioning, intended to liberate the human predictive process from preconceived lenses in data solicitation and/or interpretation. Efficacy, representativeness and generalizability are all heightened in this schema. Thus, the possible risks of biased big data arising from the inputs themselves must be acknowledged and addressed. Awareness of data deficiencies, structures for data inclusiveness, strategies for data sanitation, and mechanisms for data correction can help realize the potential of big data for a personalized medicine era. Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data.

    View details for DOI 10.1038/s41746-019-0157-2

    View details for Web of Science ID 000481541800001

    View details for PubMedID 31453373

    View details for PubMedCentralID PMC6700078

  • Orthopaedic phenotyping of NGLY1 deficiency using an international, family-led disease registry. Orphanet journal of rare diseases Cahan, E. M., Frick, S. L. 2019; 14 (1): 148


    BACKGROUND: NGLY1 deficiency is a rare autosomal recessive disorder caused by loss in enzymatic function of NGLY1, a peptide N-glycanase that has been shown to play a role in endoplasmic reticulum associated degradation (ERAD). ERAD dysfunction has been implicated in other well-described proteinopathies, such as Alzheimer's disease, Parkinson's disease, and Huntington's disease. The classical clinical tetrad includes developmental delay, hypolacrima, transiently elevated transaminases, and hyperkinetic movement disorders. The musculoskeletal system is also commonly affected, but the orthopaedic phenotype has been incompletely characterized. Best practices for orthopaedic clinical care have not been elucidated and considerable variability has resulted from this lack of evidence base. Our study surveyed patients enrolled in an international registry for NGLY1 deficiency in order to characterize the orthopaedic manifestations, sequelae, and management.RESULTS: Our findings, encompassing the largest cohort for NGLY1 deficiency to date, detail levels of motor milestone achievement; physical exam findings; fracture rates/distribution; frequency of motor skill regression; non-pharmacologic and non-procedural interventions; pharmacologic therapies; and procedural interventions experienced by 29 participants. Regarding the orthopaedic phenotype, at time of survey response, we found that over 40% of patients experienced motor skill regression from their peak. Over 80% of patients had at least one orthopaedic diagnosis, and nearly two-thirds of the total had two or more. More than half of patients older than 6years had sustained a fracture. Related to orthopaedic non-medical management, we found that 93 and 79% of patients had utilized physical therapy and non-operative orthoses, respectively. In turn, the vast majority took at least one medication (including for bone health and antispasmodic therapy). Finally, nearly half of patients had undergone an invasive procedure. Of those older than 6years, two-thirds had one or more procedures. Stratification of these analyses by sex revealed distinctive differences in disease natural history and clinical management course.CONCLUSIONS: These findings describing the orthopaedic natural history and standard of care in patients with NGLY1 deficiency can facilitate diagnosis, inform prognosis, and guide treatment recommendations in an evidence-based manner. Furthermore, the methodology is notable for its partnership with a disease-specific advocacy organization and may be generalizable to other rare disease populations. This study fills a void in the existing literature for this population and this methodology offers a precedent upon which future studies for rare diseases can build.

    View details for DOI 10.1186/s13023-019-1131-4

    View details for PubMedID 31217022

  • Students Shouldn't Merely 'Survive' Medical School. Health affairs (Project Hope) Cahan, E. M. 2019; 38 (9): 1585?88


    A medical student reflects on what can be done to address mental illness among medical trainees.

    View details for DOI 10.1377/hlthaff.2018.05356

    View details for PubMedID 31479356

  • Business Strategies to Promote Health. JAMA Cahan, E. M. 2019; 321 (21): 2133?34

    View details for DOI 10.1001/jama.2019.2799

    View details for PubMedID 31162562

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