Multi-authors' self-citation: A further impact factor bias?

Scientific Misconduct (Fraud) in Medical Writing

Scientific misconduct (fraud) in medical writing is an important and not infrequent problem for the scientific community. Although noteworthy examples of fraud surface occasionally in the media, detection of fraud in medical publishing is generally not as straightforward as one might think. National bodies on ethics in science, strict selection criteria, a robust peer-review process, careful statistical validation, and anti-plagiarism and image-fraud detection software contribute to the production of high-quality manuscripts. This article reviews the various types of fraud in medical writing, discusses the related literature, and describes tools journals implement to unmask fraud. [Orthopedics. 2018; 41(2):e176-e183].

Aberration of the Citation

Multiple inherent biases related to different citation practices (for e.g., self-citations, negative citations, wrong citations, multi-authorship-biased citations, honorary citations, circumstantial citations, discriminatory citations, selective and arbitrary citations, etc.) make citation-based bibliometrics strongly flawed and defective measures. A paper can be highly cited for a while (for e.g., under circumstantial or transitional knowledge), but years later it may appear that its findings, paradigms, or theories were untrue or invalid anymore. By contrast, a paper may remain shelved or overlooked for years or decades, but new studies or discoveries may actualize its subject at any moment. As citation-based metrics are transformed into "commercial activities," the "citation credit" should be considered on a commercial basis too, in the sense that "citation credit" should be shared out as a "citation dividend" by shareholders (coauthors) averagely or proportionally to their contributions but not fully appropriated by each of them. At equal numbers of citations, the greater number of authors, the lower "citation credit" should be and vice versa. Overlooking the presence of distorted and subjective citation practices makes many people and administrators "obsessed" with the number of citations to such an extent to run after "highly cited" authors and to create specialized citation databases for commercial purposes. Citation-based bibliometrics, however, are unreliable and unscientific measures; citation counts do not mean that a more cited work is of a higher quality or accuracy than a less cited work because citations do not measure the quality or accuracy. Citations do not mean that a highly cited author or journal is more commendable than a less cited author or journal. Citations are not more than countable numbers: no more, no less.

A generalized view of self-citation: direct, co-author, collaborative, and coercive induced self-citation

The phenomenon of self-citation can present in many different forms, including direct, co-author, collaborative, and coercive induced self-citation. It can also pertain to the citation of single scientists, groups of scientists, journals, and institutions. This article presents some case studies of extreme self-citation practices. It also discusses the implications of different types of self-citation. Self-citation is not necessarily inappropriate by default. In fact, usually it is fully appropriate but often it is even necessary. Conversely, inappropriate self-citation practices may be highly misleading and may distort the scientific literature. Coercive induced self-citation is the most difficult to discover. Coercive Induced self-citation may happen directly from reviewers of articles, but also indirectly from reviewers of grants, scientific advisors who steer a research agenda, and leaders of funding agencies who may espouse spending disproportionately large funds in research domains that perpetuate their own self-legacy. Inappropriate self-citation can be only a surrogate marker of what might be much greater distortions of the scientific corpus towards conformity to specific opinions and biases. Inappropriate self-citations eventually affect also impact metrics. Different impact metrics vary in the extent to which they can be gamed through self-citation practices. Citation indices that are more gaming-proof are available and should be more widely used. We need more empirical studies to dissect the impact of different types of inappropriate self-citation and to examine the effectiveness of interventions to limit them.

"3 . . 2 . . 1 . . Impact [factor]: target [academic career] destroyed!": just another statistical casualty

"Publish or perish" is the time-honored "principle" for academicians who race to accumulate lines under the "publications" section of a curriculum vitae. The original intent of publication-to inform others of findings and further scientific knowledge-has been corrupted by factors including (1) exponential growth of journals and the journal industry, fueled in part by intrusion of the Internet into all aspects of academic life; and (2) adoption of journal metrics (rather than written content) as the measure of scientific quality. The proprietary Thomson Reuters Impact Factor is the most pernicious metric, having caused editors and publishers to change editorial practices to boost the number. At the same time, gullible administrators and government agencies have been persuaded that metrics for the journal in which materials are published can be used as a measure of the worth of individual investigators (and institutions) and their research efforts: simple numbers can be substituted for the burdensome effort required to read and assess research quality. Thus, granting of research funds, awarding of academic rank and tenure, and determination of salaries (including bonus payments) have become tied to manipulable journal metrics rather than the significance or quality of reported research. Therefore, it is no wonder that the integrity of science is more often being questioned. How should a young investigator approach the "publish or perish" dilemma? Performing sound research and preparing optimal materials for publication must remain the overriding goals: properly articulate the question addressed by the study; thoroughly document all methods and case information; carefully describe results including any conflicting or negative findings; discuss the importance of the findings along with how the results address the initial question and whether findings refute or confirm previous studies; prepare properly cited bibliographic references; list all author contributions, potential conflicts of interest, financial support, and required ethical approvals; and provide a catchy title and an abstract containing sufficient information that other investigators perusing scientific indices will be enticed to read the published article. Submit the completed manuscript to the most appropriate journal based on that journal's previously published content and relevance to the field of study regardless of journal metrics. On publication, notify investigators in the same field to ask for their comments on the work. Thus, an individual will become known for the quality of his or her work product and the worshiping of publication metrics will be unnecessary.

Nine criteria for a measure of scientific output

Scientific research produces new knowledge, technologies, and clinical treatments that can lead to enormous returns. Often, the path from basic research to new paradigms and direct impact on society takes time. Precise quantification of scientific output in the short-term is not an easy task but is critical for evaluating scientists, laboratories, departments, and institutions. While there have been attempts to quantifying scientific output, we argue that current methods are not ideal and suffer from solvable difficulties. Here we propose criteria that a metric should have to be considered a good index of scientific output. Specifically, we argue that such an index should be quantitative, based on robust data, rapidly updated and retrospective, presented with confidence intervals, normalized by number of contributors, career stage and discipline, impractical to manipulate, and focused on quality over quantity. Such an index should be validated through empirical testing. The purpose of quantitatively evaluating scientific output is not to replace careful, rigorous review by experts but rather to complement those efforts. Because it has the potential to greatly influence the efficiency of scientific research, we have a duty to reflect upon and implement novel and rigorous ways of evaluating scientific output. The criteria proposed here provide initial steps toward the systematic development and validation of a metric to evaluate scientific output.

Scoliosis: density-equalizing mapping and scientometric analysis

Background

Publications related to scoliosis have increased enormously. A differentiation between publications of major and minor importance has become difficult even for experts. Scientometric data on developments and tendencies in scoliosis research has not been available to date. The aim of the current study was to evaluate the scientific efforts of scoliosis research both quantitatively and qualitatively.

Methods

Large-scale data analysis, density-equalizing algorithms and scientometric methods were used to evaluate both the quantity and quality of research achievements of scientists studying scoliosis. Density-equalizing algorithms were applied to data retrieved from ISI-Web.

Results

From 1904 to 2007, 8,186 items pertaining to scoliosis were published and included in the database. The studies were published in 76 countries: the USA, the U.K. and Canada being the most productive centers. The Washington University (St. Louis, Missouri) was identified as the most prolific institution during that period, and orthopedics represented by far the most productive medical discipline. "BRADFORD, DS" is the most productive author (146 items), and "DANSEREAU, J" is the author with the highest scientific impact (h-index of 27).

Conclusion

Our results suggest that currently established measures of research output (i.e. impact factor, h-index) should be evaluated critically because phenomena, such as self-citation and co-authorship, distort the results and limit the value of the conclusions that may be drawn from these measures. Qualitative statements are just tractable by the comparison of the parameters with respect to multiple linkages. In order to obtain more objective evaluation tools, new measurements need to be developed.