What Is the Performance of ChatGPT in Determining the Gender of Individuals Based on Their First and Last Names?

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Physicians' views on the usefulness of practical tools for assessing the driving ability of older drivers: a cross-sectional study

We aimed to explore Swiss physicians' views on the usefulness of a self-administered questionnaire completed by older drivers before the consultation and a reference guide summarising current Swiss guidelines on the fitness-to-drive assessment of older drivers. We also aimed to assess the frequency with which physicians used the information sources provided by the Swiss traffic medicine website

How well does NamSor perform in predicting the country of origin and ethnicity of individuals based on their first and last names?

Background: We aimed to evaluate NamSor's performance in predicting the country of origin and ethnicity of individuals based on their first/last names. Methods: We retrieved the name and country of affiliation of all authors of PubMed publications in 2021, affiliated with universities in the twenty-two countries whose researchers authored ≥1,000 medical publications and whose percentage of migrants was &lt;2.5% (N = 88,699). We estimated with NamSor their most likely "continent of origin" (Asia/Africa/Europe), "country of origin" and "ethnicity". We also examined two other variables that we created: "continent#2" ("Europe" replaced by "Europe/America/Oceania") and "country#2" ("Spain" replaced by "Spain/Hispanic American country" and "Portugal" replaced by "Portugal/Brazil"). Using "country of affiliation" as a proxy for "country of origin", we calculated for these five variables the proportion of misclassifications (= errorCodedWithoutNA) and the proportion of non-classifications (= naCoded). We repeated the analyses with a subsample consisting of all results with inference accuracy ≥50%. Results: For the full sample and the subsample, errorCodedWithoutNA was 16.0% and 12.6% for "continent", 6.3% and 3.3% for "continent#2", 27.3% and 19.5% for "country", 19.7% and 11.4% for "country#2", and 20.2% and 14.8% for "ethnicity"; naCoded was zero and 18.0% for all variables, except for "ethnicity" (zero and 10.7%). Conclusion: NamSor is accurate in determining the continent of origin, especially when using the modified variable (continent#2) and/or restricting the analysis to names with accuracy ≥50%. The risk of misclassification is higher with country of origin or ethnicity, but decreases, as with continent of origin, when using the modified variable (country#2) and/or the subsample.</p

Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference

Objective: We recently showed that genderize.io is not a sufficiently powerful gender detection tool due to a large number of nonclassifications. In the present study, we aimed to assess whether the accuracy of inference by genderize.io can be improved by manipulating the first names in the database. Methods: We used a database containing the first names, surnames, and gender of 6,131 physicians practicing in a multicultural country (Switzerland). We uploaded the original CSV file (file #1), the file obtained after removing all diacritic marks, such as accents and cedilla (file #2), and the file obtained after removing all diacritic marks and retaining only the first term of the compound first names (file #3). For each file, we computed three performance metrics: proportion of misclassifications (errorCodedWithoutNA), proportion of nonclassifications (naCoded), and proportion of misclassifications and nonclassifications (errorCoded). Results: naCoded, which was high for file #1 (16.4%), was reduced after data manipulation (file #2: 11.7%, file #3: 0.4%). As the increase in the number of misclassifications was small, the overall performance of genderize.io (i.e., errorCoded) improved, especially for file #3 (file #1: 17.7%, file #2: 13.0%, and file #3: 2.3%). Conclusions: A relatively simple manipulation of the data improved the accuracy of gender inference by genderize.io. We recommend using genderize.io only with files that were modified in this way.</p

Are acceptance and publication times longer in primary health care journals compared to internal medicine journals? A comparative study of 117 high-impact journals

Speed of publication can be used as an indicator for estimating the publication efficiency of journals. We aimed to assess whether median acceptance, publication and total times differed for articles published in internal medicine (IM) journals and those published in primary health care (PHC) journals. We extracted all articles published in 2012–2022 in all IM or PHC journals with an impact factor greater than two (N = 117 journals). We calculated for each article the acceptance time (= number of days from submission to acceptance), the publication time (= number of days from acceptance to publication), and the total time (= number of days from submission to publication). We compared median acceptation/publication/total times for IM and PHC journals using Wilcoxon rank-sum tests. There were 68,612 articles for determining acceptance times, 70,158 for publication times, and 69,831 for total times. Median submission/acceptance/total times were 41/50/89 days longer in PHC vs. IM journals (p-values &lt; 0.001). In conclusion, we found that median acceptance/publication/total times were higher in PHC vs. IM journals. This study shows that there is room for improvement in the speed of publication of articles in PHC journals

Retractions in primary care journals (2000-2022)

No study has so far examined retractions in primary care. Our aim was to assess the number/proportion of retracted articles in primary care journals and describe their main characteristics. For comparison, we also calculated the number/proportion of retractions for general internal medicine journals and for all PubMed articles. We selected the eighteen primary care journals with Journal Citation Reports (JCR) impact factor in 2021. We retrieved all PubMed articles published in these journals between January 2000 and December 2022 that were retracted. We calculated the proportion of retractions by dividing the number of retractions by the number of PubMed articles published in these journals during the same period. We also calculated the proportion of retractions for (i) all PubMed articles published in the 117 general internal medicine journals with a JCR impact factor &gt; 2 in 2021 and (ii) all PubMed articles. We found seven retractions among the 52,453 PubMed articles published in the eighteen primary care journals. The proportion of retractions (= 0.013%) was about two times lower than for articles published in internal medicine journals (= 0.028%) and about four times lower than for all PubMed articles (= 0.056%). Four articles were retracted for misconduct, two for unintentional errors and one for another reason. Although it may be explained by a particularly high level of scientific rigour and integrity among primary care researchers, the low number of retractions in primary care journals raises questions about the effectiveness of retraction measures in these journals