Deep forecasting of translational impact in medical research

The value of biomedical research--a $1.7 trillion annual investment--is ultimately determined by its downstream, real-world impact. Current objective predictors of impact rest on proxy, reductive metrics of dissemination, such as paper citation rates, whose relation to real-world translation remains unquantified. Here we sought to determine the comparative predictability of future real-world translation--as indexed by inclusion in patents, guidelines or policy documents--from complex models of the abstract-level content of biomedical publications versus citations and publication meta-data alone. We develop a suite of representational and discriminative mathematical models of multi-scale publication data, quantifying predictive performance out-of-sample, ahead-of-time, across major biomedical domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990 to 2019, encompassing 43.3 million papers across all domains. We show that citations are only moderately predictive of translational impact as judged by inclusion in patents, guidelines, or policy documents. By contrast, high-dimensional models of publication titles, abstracts and metadata exhibit high fidelity (AUROC > 0.9), generalise across time and thematic domain, and transfer to the task of recognising papers of Nobel Laureates. The translational impact of a paper indexed by inclusion in patents, guidelines, or policy documents can be predicted--out-of-sample and ahead-of-time--with substantially higher fidelity from complex models of its abstract-level content than from models of publication meta-data or citation metrics. We argue that content-based models of impact are superior in performance to conventional, citation-based measures, and sustain a stronger evidence-based claim to the objective measurement of translational potential

Voting Technology, Vote-by-Mail, and Residual Votes in California, 1990-2010

This paper examines how the growth in vote-by-mail and changes in voting technologies led to changes in the residual vote rate in California from 1990 to 2010. We find that in California’s presidential elections, counties that abandoned punch cards in favor of optical scanning enjoyed a significant improvement in the residual vote rate. However, these findings do not always translate to other races. For instance, find that the InkaVote system in Los Angeles has been a mixed success, performing very well in presidential and gubernatorial races, fairly well for ballot propositions, and poorly in Senate races. We also conduct the first analysis of the effects of the rise of vote-by-mail on residual votes. Regardless of the race, increased use of the mails to cast ballots is robustly associated with a rise in the residual vote rate. The effect is so strong that the rise of voting by mail in California has mostly wiped out all the reductions in residual votes that were due to improved voting technologies since the early 1990s

Origami voting: a non-cryptographic approach to transparent ballot verification

International audienceOver the past four decades, fear of election manipulation and hacking has spurred the security technology community to propose a variety of voting systems to implement verifiable voting. Most of these rely on hard to understand cryptographic protocols, which can affect whether users actually verify their selections. Three-Ballot and Vote/Anti-Vote/Vote, two related systems among the few non-cryptographic end-to-end verifiable voting systems, made improvements in security while eliminating complex protocols. They unfortunately suffered from usability issues, and although they did not require cryptographic primitives, they still relied on electronic devices. To address this, we introduce three folded-paper based systems that allow verifiable voting and resist common attacks despite not relying on any cryptography or electronic devices. The proposals are based on 1) semi-translucent ballots, 2) masking tape, or 3) folding and punching. These Origami voting methods help users understand the underlying mechanisms and give them a direct geometric approach to verification

Chlorhexidine versus povidone–iodine skin antisepsis before upper limb surgery (CIPHUR) : an international multicentre prospective cohort study

Introduction Surgical site infection (SSI) is the most common and costly complication of surgery. International guidelines recommend topical alcoholic chlorhexidine (CHX) before surgery. However, upper limb surgeons continue to use other antiseptics, citing a lack of applicable evidence, and concerns related to open wounds and tourniquets. This study aimed to evaluate the safety and effectiveness of different topical antiseptics before upper limb surgery. Methods This international multicentre prospective cohort study recruited consecutive adults and children who underwent surgery distal to the shoulder joint. The intervention was use of CHX or povidone–iodine (PVI) antiseptics in either aqueous or alcoholic form. The primary outcome was SSI within 90 days. Mixed-effects time-to-event models were used to estimate the risk (hazard ratio (HR)) of SSI for patients undergoing elective and emergency upper limb surgery. Results A total of 2454 patients were included. The overall risk of SSI was 3.5 per cent. For elective upper limb surgery (1018 patients), alcoholic CHX appeared to be the most effective antiseptic, reducing the risk of SSI by 70 per cent (adjusted HR 0.30, 95 per cent c.i. 0.11 to 0.84), when compared with aqueous PVI. Concerning emergency upper limb surgery (1436 patients), aqueous PVI appeared to be the least effective antiseptic for preventing SSI; however, there was uncertainty in the estimates. No adverse events were reported. Conclusion The findings align with the global evidence base and international guidance, suggesting that alcoholic CHX should be used for skin antisepsis before clean (elective upper limb) surgery. For emergency (contaminated or dirty) upper limb surgery, the findings of this study were unclear and contradict the available evidence, concluding that further research is necessary

Replication data for: The Butterfly Did It: The Aberrant Vote for Buchanan in Palm Beach County, Florida

We show that the butterfly ballot used in Palm Beach County, Florida, in the 2000 presidential election caused more than 2,000 Democratic voters to vote by mistake for Reform candidate Pat Buchanan, a number larger than George W. Bush’s certified margin of victory in Florida. We use multiple methods and several kinds of data to rule out alternative explanations for the votes Buchanan received in Palm Beach County. Among 3,053 U.S. counties where Buchanan was on the ballot, Palm Beach County has the most anomalous excess of votes for him. In Palm Beach County, Buchanan’s proportion of the vote on election-day ballots is four times larger than his proportion on absentee (nonbutterfly) ballots, but Buchanan’s proportion does not differ significantly between election-day and absentee ballots in any other Florida county. Unlike other Reform candidates in Palm Beach County, Buchanan tended to receive election-day votes in Democratic precincts and from individuals who voted for the Democratic U.S. Senate candidate. Robust estimation of overdispersed binomial regress ion models underpins much of the analysis

Deep forecasting of translational impact in medical research

The value of biomedical research--a $1.7 trillion annual investment--is ultimately determined by its downstream, real-world impact. Current objective predictors of impact rest on proxy, reductive metrics of dissemination, such as paper citation rates, whose relation to real-world translation remains unquantified. Here we sought to determine the comparative predictability of future real-world translation--as indexed by inclusion in patents, guidelines or policy documents--from complex models of the abstract-level content of biomedical publications versus citations and publication meta-data alone. We develop a suite of representational and discriminative mathematical models of multi-scale publication data, quantifying predictive performance out-of-sample, ahead-of-time, across major biomedical domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990 to 2019, encompassing 43.3 million papers across all domains. We show that citations are only moderately predictive of translational impact as judged by inclusion in patents, guidelines, or policy documents. By contrast, high-dimensional models of publication titles, abstracts and metadata exhibit high fidelity (AUROC > 0.9), generalise across time and thematic domain, and transfer to the task of recognising papers of Nobel Laureates. The translational impact of a paper indexed by inclusion in patents, guidelines, or policy documents can be predicted--out-of-sample and ahead-of-time--with substantially higher fidelity from complex models of its abstract-level content than from models of publication meta-data or citation metrics. We argue that content-based models of impact are superior in performance to conventional, citation-based measures, and sustain a stronger evidence-based claim to the objective measurement of translational potential.Comment: 28 pages, 6 figure