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Acta Neurochirurgica

, Volume 161, Issue 2, pp 205–211 | Cite as

Association between medical academic genealogy and publication outcome: impact of unconscious bias on scientific objectivity

  • Brian R. Hirshman
  • Ali A. Alattar
  • Sanjay Dhawan
  • Kathleen M. Carley
  • Clark C. ChenEmail author
Original Article - Neurosurgery general
  • 37 Downloads
Part of the following topical collections:
  1. Neurosurgery general

Abstract

Background

Our previous studies suggest that the training history of an investigator, termed “medical academic genealogy”, influences the outcomes of that investigator’s research. Here, we use meta-analysis and quantitative statistical modeling to determine whether such effects contribute to systematic bias in published conclusions.

Methods

A total of 108 articles were identified through a comprehensive search of the high-grade glioma (HGG) surgical resection literature. Analysis was performed on the 70 articles with sufficient data for meta-analysis. Pooled estimates were generated for key academic genealogies. Monte Carlo simulations were performed to determine whether the effects attributed to genealogy alone can arise due to chance alone.

Results

Meta-analysis of the HGG literature without consideration for academic medical genealogy revealed that gross total resection (GTR) was associated with a significant decrease in the odds ratio (OR) for the hazard of death after surgery for both anaplastic astrocytoma (AA) and glioblastoma (AA: log [OR] = − 0.04, 95% CI [− 0.07 to − 0.01]; glioblastoma log [OR] = − 0.36, 95% CI [− 0.44 to − 0.29]). For the glioblastoma literature, meta-analysis of articles contributed by members of a genealogy consisting of mostly radiation oncologists revealed no reduction in the hazard of death after GTR [log [OR] = − 0.16, 95% CI [− 0.41 to 0.09]. In contrast, meta-analysis of published articles contributed by members of a genealogy consisting of mostly neurosurgeons revealed that GTR was associated with a significant reduction in the hazard of death [log [OR] = − 0.29, 95% CI [− 0.40 to 0.18]. Monte Carlo simulation revealed that the observed discrepancy between the articles contributed by the members of these two genealogies was unlikely to arise by chance alone (p < 0.006).

Conclusions

Meta-analysis of articles contributed by authors belonging to the different medical academic genealogies yielded distinct and contradictory pooled point-estimates, suggesting that genealogy contributes to systematic bias in the published literature.

Keywords

Medical academic genealogy Scientific objectivity Meta-analysis Brain tumor 

Notes

Compliance with ethical standards

Informed consent

For this type of study, formal consent is not required.

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.

Ethical approval

A formal approval by the institutional ethics committee was not required for this study since all articles, journals, and author data were collected from publicly available sources.

Animal experiments

Ethical approval: This article does not contain any studies with animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Brian R. Hirshman
    • 1
    • 2
    • 3
  • Ali A. Alattar
    • 4
  • Sanjay Dhawan
    • 5
  • Kathleen M. Carley
    • 2
    • 3
  • Clark C. Chen
    • 5
    Email author
  1. 1.Department of NeurosurgeryUniversity of California San DiegoSan DiegoUSA
  2. 2.Center for Computational Analysis of Social and Organizational Systems, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  3. 3.Computation, Organizations and Society Program, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA
  4. 4.School of MedicineUniversity of California San DiegoSan DiegoUSA
  5. 5.Department of NeurosurgeryUniversity of MinnesotaMinneapolisUSA

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