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The distributions of the blue and red circles in the graphs make it clearly visible that for nearly all of the manuscripts, values for the reviewers' ratings above a certain predictor cutoff led to acceptance by the AC-IE editor and values below the cutoff led to rejection.
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Another way of performing the analysis is from the point of associating the measured data with certain predictor variables in a linear mixed-effects model as we will explain below.
It is possible that certain predictor variables are associated with increased quality of reporting.
Moreover, the administration of the baseline questionnaire may have heightened patients' awareness, and may have influenced certain predictor variables.
It is possible that non-significant results observed for certain predictor variables could be due to small sample sizes within these cells.
Several problems arise from this process, including the loss of fine-grained differences for certain predictor and outcome variable levels which may have resulted in slightly different make-up of significant regression models.
While the overall sample size of the study allowed sufficient power to reduce the probability for a Type II error in the primary analysis, it is possible that the sample sizes for certain predictor variables (i.e. Aboriginal status) were not sufficiently large to establish a statistically significant.
In addition, alternative ways of coding certain predictors (eg, a dichotomised household WI) might have revealed interaction effects that are not evident with the present methodology.
Tables 1 and 2 also present results from the bootstrap analyses including the percentage of time that certain predictors appeared in the bootstrap sample (a bootstrap "hit"), the percentage of hits that passed the food screen, and the percentage of hits significant in the final model.
There could be an association between the clinical outcome of interest and the missingness of certain predictors.
The implication of these biases on the cumulative evidence of this review is unclear, and as a result, it is important to consider how publication bias can under- and overestimate certain predictors of longevity.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com