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For the last two measures (publications in all AIDS/HIV journals and in journals with IF ≥3), conference publications were excluded.
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A second approach to detecting and measuring publication bias is to estimate a meta-regression with standard errors or precision as the sole covariate.
In part because it is the easiest to measure, publication productivity--the number of publications--is often the first item evaluators look at when reviewing the curricula vitae (CVs) of job applicants and tenure candidates (Steinpreis et al., 1999).
Indeed, official rankings of universities often include parameters measuring publication rates [39] (although the validity of such rankings is controversial [40], [41]).
Lastly, we measured publication biases of the eligible studies using funnel plots.
However, some other academic conferences included publication rates as an objective of their conference, and conducted post-conference database searches to measure publication rates, which was not included in our study (James 2001; Scott 2005; Saito et al. 2009).
Begg and Mazumdar's proposed adjusted rank correlation test [ 14] and Egger's linear regression approach [ 15] were used to measure publication bias, which was shown as a funnel plot.
We attempted to measure publication bias that is, a lack of small studies without significant results—by the linear regression test for funnel plot asymmetry described by Egger et al. (1997).
Measuring publication bias in animal studies is challenging because not only do few registries of animal studies exist [ 14] but details about the methods of animal studies are difficult to obtain from animal ethics review committees and regulatory authorities.
The objectives of the current study are to quantify the prevalence and predictors of anti-TNF use, and to describe steroid use in a nationally representative US sample of patients ≥65 years with IBD, immediately prior to quality measure publication.
Even with commonly used outcome measures, trial publications still differ in their reporting.
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