What Previum returns on a published nursing paper: every critique pinned to its sentence, p values recomputed, figures that do not add up and references checked against eight databases.
Sample report · nursing · real, already published paper
A paper published in a peer-reviewed journal, run through the Basic report exactly as it is, nothing edited: the manuscript with every critique pinned to the sentence that triggers it, and the code checks underneath.
The paper has already been peer reviewed and published. It is reproduced here under its Creative Commons Attribution 4.0 license (the full attribution, authors included, is at the linked DOI; they are deliberately not named here) to show what Previum returns on a real text from your field. The objections are the ones one more reviewer could have raised; that a published paper receives them says how demanding the tool is, not how weak the paper is.
The effect of multimedia-based education on nurses’ clinical capability in radiation protection in operating rooms: a randomized controlled study. BMC Nursing, 2025. DOI: 10.1186/s12912-025-04108-3. License: CC BY 4.0.
The manuscript reports a well-registered RCT with clinically relevant outcomes and large effect sizes, yet critical statistical reporting and analysis errors prevent firm conclusions. The two most urgent problems are the impossible ANCOVA degrees of freedom and the mismatched sample-size calculation. Both must be corrected before the primary claims can be trusted. Minor issues of self-report bias and sequential recruitment further limit interpretation. With transparent re-analysis the paper could become publishable in a mid-tier nursing or health-education journal.
Quoted sentence: «Based on a previous single-group pre–post study by Dauer et al. [17], the mean (SD) knowledge scores regarding radiation protection among nurses were 59 (13) before the intervention and 71 (20) after the intervention. Therefore, the sample size was calculated based on a paired-sample t-test using G*Power version 3.1.»
Why it matters: The calculated n=50 per group may be under-powered for the actual between-group contrasts that were performed, especially given non-normal distributions and multiple subscales.
What would fix it: Report a post-hoc power analysis or sensitivity analysis for the observed between-group effect sizes (Cohen’s d 0.79–2.73) using the actual two-group repeated-measures design; if under-powered, discuss this explicitly in the limitations.
Quoted sentence: «The ANCOVA revealed no significant main effects for group (F(1, 294) = 4.343, p = 0.173, η² = 0.015) or time (F(2, 294) = 0.810, p = 0.552, η² = 0.448) on clinical capability scores. However, a significant group × time interaction was found (F(2, 294) = 32.845, p < 0.001, η² = 0.183).»
Why it matters: The reported group-by-time interaction (F=32.845) cannot be interpreted reliably, undermining the primary statistical claim that improvement over time was greater in the intervention arm.
What would fix it: Re-analyze the total score (or subscale totals) with a linear mixed-effects model that includes random intercepts for participants, fixed effects for group, time, and group×time, and report appropriate F or likelihood-ratio tests with correct degrees of freedom and partial eta-squared or marginal R² values.
In total the reviewer found 7 major and 9 minor issues; the Basic report shows the two that decide.
With the same engine the tool uses, on 11 September 2026: the full text of the paper, tables and references included, went into the Basic report with no added instructions. The checks below are arithmetic on the text and queries to eight bibliographic databases, no model involved. Not a line of the output has been edited.
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