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Esteemed Colleagues: A Model of the Effect of Open Data on Selective Reporting of Scientific Results

ABSTRACT: Open data, the practice of making available to the research community the underlying data and analysis codes used to generate scientific results, facilitates verification of published results, and should thereby reduce the expected benefit (and hence the incidence) of p-hacking and other forms of academic dishonesty. This paper presents a simple signaling model of how this might work in the presence of two kinds of cost. First, reducing the cost of “checking the math” increases verification and reduces falsification. Cases where the author can choose a high or low verification-cost regime (that is, open or closed data) result in unraveling; not all authors choose the low-cost route, but the best do. The second kind of cost is the cost to authors of preparing open data. Introducing these costs results in that high- and low-quality results being published in both open and closed data regimes, but even when the costs are independent of research quality open data is favored by high-quality results in equilibrium. A final contribution of the model is a measure of “science welfare” that calculates the ex-post distortion of equilibrium beliefs about the quality of published results, and shows that open data will always improve the aggregate state of knowledge.

SUBMITTER: Spiegelman E

PROVIDER: S-EPMC8566335 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAntimicrobial stewardship (AMS) strategies worldwide focus on optimising the use of antibiotics. Selective susceptibility reporting is recommended as an effective AMS tool although there is a lack of representative studies investigating the impact of selective susceptibility reporting on antibiotic use. The aim of this study was to investigate the impact of selective susceptibility reporting of Staphylococcus aureus (S. aureus) on antibiotic consumption. Enhancing the use of narrow-spectrum beta-lactam antibiotics such as flucloxacillin/cefazolin/cefalexin is one of the main goals in optimising antibiotic therapy of S. aureus infections.MethodsThis interventional study with control group was conducted at a tertiary care hospital in Germany. During the one-year interventional period susceptibility reports for all methicillin-sensitive S. aureus (MSSA) were restricted to flucloxacillin/cefazolin/cefalexin, trimethoprim-sulfamethoxazole, clindamycin, gentamicin and rifampin/fosfomycin, instead of reporting all tested antibiotics. The impact of implementing selective reporting was analysed by monitoring total monthly antibiotic consumption in our hospital and in a reference hospital (recommended daily dose/100 occupied bed days: RDD/100 BD), as well as on an individual patient level by analysing days of therapy adjusted for bed days (DOT/ 100 BD) for patients with S. aureus bacteremia (SAB) and respectively skin and soft tissue infections (SSTI).ResultsMSSA-antibiograms were acquired for 2836 patients. The total use of narrow-spectrum beta-lactams more than doubled after implementing selective reporting (from 1.2 to 2.8 RDD/100 BD, P < 0.001). The use of intravenous flucloxacillin/cefazolin for SAB rose significantly from 52 to 75 DOT/100 BD (plus 42%), just as the use of oral cefalexin for SSTI (from 1.4 to 9.4 DOT/100 BD, from 3 to 17 of 85/88 patients). Considering the overall consumption, there was no decrease in antibiotics omitted from the antibiogram. This was probably due to their wide use for other infections.ConclusionsAs narrow-spectrum beta-lactams are not widely used for other infections, their increase in the overall consumption of the entire hospital was a strong indicator that selective reporting guided clinicians to an optimised antibiotic therapy of S. aureus infections. On a patient level, this assumption was verified by a significant improved treatment of S. aureus infections in the subgroups of SAB and SSTI. As useful AMS tool, we recommend implementing selective reporting rules into the national/international standards for susceptibility reporting.

Project description:BACKGROUND: There is a growing need for efficient and integrated access to databases provided by diverse institutions. Using a linked data design pattern allows the diverse data on the Internet to be linked effectively and accessed efficiently by computers. Previously, we developed the Allie database, which stores pairs of abbreviations and long forms (LFs, or expanded forms) used in the life sciences. LFs define the semantics of abbreviations, and Allie provides a Web-based search service for researchers to look up the LF of an unfamiliar abbreviation. This service encounters two problems. First, it does not display each LF's definition, which could help the user to disambiguate and learn the abbreviations more easily. Furthermore, there are too many LFs for us to prepare a full dictionary from scratch. On the other hand, DBpedia has made the contents of Wikipedia available in the Resource Description Framework (RDF), which is expected to contain a significant number of entries corresponding to LFs. Therefore, linking the Allie LFs to DBpedia entries may present a solution to the Allie's problems. This requires a method that is capable of matching large numbers of string pairs within a reasonable period of time because Allie and DBpedia are frequently updated. RESULTS: We built a Linked Open Data set that links LFs to DBpedia titles by applying key collision methods (i.e., fingerprint and n-gram fingerprint) to their literals, which are simple approximate string-matching methods. In addition, we used UMLS resources to normalise the life science terms. As a result, combining the key collision methods with the domain-specific resources performed best, and 44,027 LFs have links to DBpedia titles. We manually evaluated the accuracy of the string matching by randomly sampling 1200 LFs, and our approach achieved an F-measure of 0.98. In addition, our experiments revealed the following. (1) Performances were similar independently from the frequency of the LFs in MEDLINE. (2) There is a relationship (r2 = 0.96, P < 0.01) between the occurrence frequencies of LFs in MEDLINE and their presence probabilities in DBpedia titles. CONCLUSIONS: The obtained results help Allie users locate the correct LFs. Because the methods are computationally simple and yield a high performance and because the most frequently used LFs in MEDLINE appear more often in DBpedia titles, we can continually and reasonably update the linked dataset to reflect the latest publications and additions to DBpedia. Joining LFs between scientific literature and DBpedia enables cross-resource exploration for mutual benefits.

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Esteemed Colleagues: A Model of the Effect of Open Data on Selective Reporting of Scientific Results

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