Reducing Quotation Errors in Scientific Manuscripts: A Novel Approach from the Global Andrology Forum

This study investigated 1) the frequency of quotation errors in multi-authored medical manuscripts in andrology, 2) analyzed common types of quotation errors and the methods used to rectify them, and 3) evaluated their impact on manuscript accuracy, credibility, and research conclusions.

Twelve manuscripts written by the Global Andrology Forum (GAF) members between 2023 and 2024 were randomly selected for this study. The manuscripts and "Quotation Verification Sheets" were analyzed by senior GAF researchers to detect the number and types of quotation errors. The error rate was calculated by the total number of quotation errors and total number of all cited references in each manuscript. The impact on manuscript sections was assessed using a 0-4 grading scale. The Spearman correlation test was used to assess the correlation between scalar variables, and the Mann-Whitney U test was utilized to compare scalar variables between two groups.

The median value of quotation errors was 10.3%. Factual inaccuracy was the most common type of error, and was observed in all twelve manuscripts at various rates. The number of errors was significantly associated with the number of references (ρ=0.706; p=0.010) and in-text citations (ρ=0.636; p=0.026). Factual inaccuracy (ρ=0.588; p=0.044) and factual interpretation (ρ=0.861; p=0.013) were also correlated with the total number of quotation errors. However, no significant associations were found between quotation errors and author numbers or their qualifications. The quotation errors adversely impacted the manuscript discussion, followed by the overall message.

Quotation errors are common in multi-authored medical manuscripts in andrology-related scientific articles. Journal editorial offices should incorporate quotation verification into the review process. Limiting references and in-text citations to only strictly necessary ones may help improve quotation accuracy. The quotation verification model proposed by GAF offers a practical and structured approach for detecting and correcting quotation errors.