Systematic review of SGLT2 receptor inhibitors in dual or triple therapy in type 2 diabetes

Background Despite the number of medications for type 2 diabetes, many people with the condition do not achieve good glycaemic control. Some existing glucose-lowering agents have adverse effects such as weight gain or hypoglycaemia. Type 2 diabetes tends to be a progressive disease, and most patients require treatment with combinations of glucose-lowering agents. The sodium glucose co-transporter 2 (SGLT2) receptor inhibitors are a new class of glucose-lowering agents. Objective To assess the clinical effectiveness and safety of the SGLT2 receptor inhibitors in dual or triple therapy in type 2 diabetes. Data sources MEDLINE, Embase, Cochrane Library (all sections); Science Citation Index; trial registries; conference abstracts; drug regulatory authorities; bibliographies of retrieved papers. Inclusion criteria Randomised controlled trials of SGLT2 receptor inhibitors compared with placebo or active comparator in type 2 diabetes in dual or combination therapy. Methods Systematic review. Quality assessment used the Cochrane risk of bias score. Results Seven trials, published in full, assessed dapagliflozin and one assessed canagliflozin. Trial quality appeared good. Dapagliflozin 10 mg reduced HbA1c by −0.54% (weighted mean differences (WMD), 95% CI −0.67 to −0.40) compared to placebo, but there was no difference compared to glipizide. Canagliflozin reduced HbA1c slightly more than sitagliptin (up to −0.21% vs sitagliptin). Both dapagliflozin and canagliflozin led to weight loss (dapagliflozin WMD −1.81 kg (95% CI −2.04 to −1.57), canagliflozin up to −2.3 kg compared to placebo). Limitations Long-term trial extensions suggested that effects were maintained over time. Data on canagliflozin are currently available from only one paper. Costs of the drugs are not known so cost-effectiveness cannot be assessed. More data on safety are needed, with the Food and Drug Administration having concerns about breast and bladder cancers. Conclusions Dapagliflozin appears effective in reducing HbA1c and weight in type 2 diabetes, although more safety data are needed

Open versus blind peer review: is anonymity better than transparency?

Peer review is widely accepted as essential to ensuring scientific quality in academic journals, yet little training is provided in the specifics of how to conduct peer review. In this article we describe the different forms of peer review, with a particular focus on the differences between single-blind, double-blind and open peer review, and the advantages and disadvantages of each. These illustrate some of the challenges facing the community of authors, editors, reviewers and readers in relation to the process of peer review. We also describe other forms of peer review, such as post-publication review, transferable review and collaborative review, and encourage clinicians and academics at all training stages to engage in the practice of peer review as part of continuing professional development

Double-blind reviewing and gender biases at EvoLang conferences

A previous study of reviewing at the Evolution of Language conferences found effects that suggested that gender bias against female authors was alleviated under double-blind review at EvoLang 11. We update this analysis in two specific ways. First, we add data from the most recent EvoLang 12 conference, providing a comprehensive picture of the conference over five iterations. Like EvoLang 11, EvoLang 12 used double-blind review, but EvoLang 12 showed no significant difference in review scores between genders. We discuss potential explanations for why there was a strong effect in EvoLang 11, which is largely absent in EvoLang 12. These include testing whether readability differs between genders, though we find no evidence to support this. Although gender differences seem to have declined for EvoLang 12, we suggest that double-blind review provides a more equitable evaluation process

Uncovering Latent Biases in Text: Method and Application to Peer Review

Quantifying systematic disparities in numerical quantities such as employment rates and wages between population subgroups provides compelling evidence for the existence of societal biases. However, biases in the text written for members of different subgroups (such as in recommendation letters for male and non-male candidates), though widely reported anecdotally, remain challenging to quantify. In this work, we introduce a novel framework to quantify bias in text caused by the visibility of subgroup membership indicators. We develop a nonparametric estimation and inference procedure to estimate this bias. We then formalize an identification strategy to causally link the estimated bias to the visibility of subgroup membership indicators, provided observations from time periods both before and after an identity-hiding policy change. We identify an application wherein "ground truth" bias can be inferred to evaluate our framework, instead of relying on synthetic or secondary data. Specifically, we apply our framework to quantify biases in the text of peer reviews from a reputed machine learning conference before and after the conference adopted a double-blind reviewing policy. We show evidence of biases in the review ratings that serves as "ground truth", and show that our proposed framework accurately detects these biases from the review text without having access to the review ratings

The Role of Author Identities in Peer Review

There is widespread debate on whether to anonymize author identities in peer review. The key argument for anonymization is to mitigate bias, whereas arguments against anonymization posit various uses of author identities in the review process. The Innovations in Theoretical Computer Science (ITCS) 2023 conference adopted a middle ground by initially anonymizing the author identities from reviewers, revealing them after the reviewer had submitted their initial reviews, and allowing the reviewer to change their review subsequently. We present an analysis of the reviews pertaining to the identification and use of author identities. Our key findings are: (I) A majority of reviewers self-report not knowing and being unable to guess the authors' identities for the papers they were reviewing. (II) After the initial submission of reviews, 7.1% of reviews changed their overall merit score and 3.8% changed their self-reported reviewer expertise. (III) There is a very weak and statistically insignificant correlation of the rank of authors' affiliations with the change in overall merit; there is a weak but statistically significant correlation with respect to change in reviewer expertise. We also conducted an anonymous survey to obtain opinions from reviewers and authors. The main findings from the 200 survey responses are: (i) A vast majority of participants favor anonymizing author identities in some form. (ii) The "middle-ground" initiative of ITCS 2023 was appreciated. (iii) Detecting conflicts of interest is a challenge that needs to be addressed if author identities are anonymized. Overall, these findings support anonymization of author identities in some form (e.g., as was done in ITCS 2023), as long as there is a robust and efficient way to check conflicts of interest

Open science and modified funding lotteries can impede the natural selection of bad science.

Assessing scientists using exploitable metrics can lead to the degradation of research methods even without any strategic behaviour on the part of individuals, via 'the natural selection of bad science.' Institutional incentives to maximize metrics like publication quantity and impact drive this dynamic. Removing these incentives is necessary, but institutional change is slow. However, recent developments suggest possible solutions with more rapid onsets. These include what we call open science improvements, which can reduce publication bias and improve the efficacy of peer review. In addition, there have been increasing calls for funders to move away from prestige- or innovation-based approaches in favour of lotteries. We investigated whether such changes are likely to improve the reproducibility of science even in the presence of persistent incentives for publication quantity through computational modelling. We found that modified lotteries, which allocate funding randomly among proposals that pass a threshold for methodological rigour, effectively reduce the rate of false discoveries, particularly when paired with open science improvements that increase the publication of negative results and improve the quality of peer review. In the absence of funding that targets rigour, open science improvements can still reduce false discoveries in the published literature but are less likely to improve the overall culture of research practices that underlie those publications

"On Hochberg et al.'s, the tragedy of the reviewers commons"

We discuss each of the recommendations made by Hochberg et al. (2009) to prevent the “tragedy of the reviewer commons”. Having scientific journals share a common database of reviewers would be to recreate a bureaucratic organization, where extra-scientific considerations prevailed. Pre-reviewing of papers by colleagues is a widespread practice but raises problems of coordination. Revising manuscripts in line with all reviewers’ recommendations presupposes that recommendations converge, which is acrobatic. Signing an undertaking that authors have taken into accounts all reviewers’ comments is both authoritarian and sterilizing. Sending previous comments with subsequent submissions to other journals amounts to creating a cartel and a single all-encompassing journal, which again is sterilizing. Using young scientists as reviewers is highly risky: they might prove very severe; and if they have not yet published themselves, the recommendation violates the principle of peer review. Asking reviewers to be more severe would only create a crisis in the publishing houses and actually increase reviewers’ workloads. The criticisms of the behavior of authors looking to publish in the best journals are unfair: it is natural for scholars to try to publish in the best journals and not to resign themselves to being second rate. Punishing lazy reviewers would only lower the quality of reports: instead, we favor the idea of paying reviewers “in kind” with, say, complimentary books or papers.Reviewer;Referee;Editor;Publisher;Publishing;Tragedy of the Commons;Hochberg

Nobel and Novice: Author Prominence Affects Peer Review

Peer-review is a well-established cornerstone of the scientific process, yet it is not immune to status bias. Merton identified the problem as one in which prominent researchers get disproportionately great credit for their contribution while relatively unknown researchers get disproportionately little credit.1 We measure the extent of this effect in the peer-review process through a pre-registered field experiment. We invite more than 3,300 researchers to review a paper jointly written by a prominent author – a Nobel laureate – and by a relatively unknown author – an early-career research associate –, varying whether reviewers see the prominent author’s name, an anonymized version of the paper, or the less well-known author’s name. We find strong evidence for the status bias: while only 23 percent recommend “reject” when the prominent researcher is the only author shown, 48 percent do so when the paper is anonymized, and 65 percent do so when the little-known author is the only author shown. Our findings complement and extend earlier results on double-anonymized vs. singleanonymized review2,3,4,5,6,7 and strongly suggest that double-anonymization is a minimum requirement for an unbiased review process