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ObjectiveHow researchers’ contributions relate to author order on the byline remains unclear. We sought to identify researchers’ contributions associated with author order, and to explore the existence of author profiles.DesignObservational study.SettingPublished record.Participants1139 authors of 119 research articles published in 2015 in the Annals of Internal Medicine.Primary outcomesPresence or absence of 10 contributions, reported by each author, published in the journal.ResultsOn average, first authors reported 7.1 contributions, second authors 5.2, middle authors 4.0, penultimate authors 4.5 and last authors 6.4 (p<0.001). The first author made the greatest contributions to drafting the article, designing the study, analysing and interpreting the data, and providing study materials or patients. The second author contributed to data analysis as well and to drafting the article. The last author was most involved in obtaining the funding, critically revising the article, designing the study and providing support. Factor analysis yielded three author profiles—Thinker (study design, revision of article, obtaining funding), Soldier (providing material or patients, providing administrative and logistical support, collecting data) and Scribe (analysis and interpretation of data, drafting the article, statistical expertise). These profiles do not strictly correspond to byline position.ConclusionsFirst, second and last authors of research articles made distinct contributions to published research. Three authorship profiles can be used to summarise author contributions. These findings shed light on the organisation of clinical research teams and may help researchers discuss, plan and report authorship in a more transparent way.

BackgroundSocioeconomic disadvantage is associated with an increased risk of adverse diabetes outcomes. In Switzerland, a country with theoretical universal healthcare coverage, people without health insurance face barriers in accessing to and in receiving standard quality care. The Geneva University Hospitals (HUG) have implemented policies aiming at reducing these gaps. We compared quality of diabetes care and ambulatory healthcare services utilization among insured and uninsured diabetic patients.MethodsThis retrospective study linked health and administrative data of type 2 diabetic outpatients with at least one HbA1c test performed in 2012–2013 at HUG. Quality of care evaluation relied on processes (annual serum HbA1c, cholesterol and microalbuminuria tesing) and outcomes (HbA1c) assessment. Healthcare utilization was assessed by the number of ambulatory clinical and laboratory visits. Results were stratified by disease course (newly diagnosed versus prevalent diabetes).ResultsOf the 198 patients included, 80 (40.4 %) were uninsured. Both groups underwent annual testing of HbA1c, cholesterol, kidney function and microalbuminuria at comparably high rates and numbers of ambulatory visits did not significantly differ. After adjustments for age and sex, there were no significant differences in serum HbA1c between groups both in those with prevalent or with newly diagnosed diabetes. Initial medical intervention entailed comparable glycaemic improvement after 6 months in incident diabetes among insured and uninsured patients.ConclusionsThis study did not find any difference in quality of diabetes care between insured and uninsured patients in a public hospital enforcing health-equity policies for access to and for delivery of standard diabetes care. It highlights the frontline role of public hospitals in contributing to care delivery equity even in countries with theoretical universal healthcare coverage.

BackgroundThe usual kappa statistic requires that all observations be enumerated. However, in free-response assessments, only positive (or abnormal) findings are notified, but negative (or normal) findings are not. This situation occurs frequently in imaging or other diagnostic studies. We propose here a kappa statistic that is suitable for free-response assessments.MethodWe derived the equivalent of Cohen’s kappa statistic for two raters under the assumption that the number of possible findings for any given patient is very large, as well as a formula for sampling variance that is applicable to independent observations (for clustered observations, a bootstrap procedure is proposed). The proposed statistic was applied to a real-life dataset, and compared with the common practice of collapsing observations within a finite number of regions of interest.ResultsThe free-response kappa is computed from the total numbers of discordant (b and c) and concordant positive (d) observations made in all patients, as 2d/(b + c + 2d). In 84 full-body magnetic resonance imaging procedures in children that were evaluated by 2 independent raters, the free-response kappa statistic was 0.820. Aggregation of results within regions of interest resulted in overestimation of agreement beyond chance.ConclusionsThe free-response kappa provides an estimate of agreement beyond chance in situations where only positive findings are reported by raters.