Sex differences in authorship in cardiothoracic surgery during the early coronavirus disease 2019 pandemic
Bibliographic record
Abstract
Objectives: The coronavirus disease 2019 (COVID-19) pandemic negatively impacted cardiothoracic (CT) surgery, with changes in clinical, academic, and personal responsibilities. We hypothesized that the pandemic may disproportionately impact female academic CT surgeons, accentuating preexisting sex disparities. This study assessed sex differences in authorship of 2 major CT surgery journals during the early part of the COVID-19 pandemic. Methods: between April and August of 2019 and the same period in 2020 were reviewed. Article type and author characteristics were obtained from the journals. Author sex was predicted using a validated multinational database (Genderize.io) and verified with authors' institutional and public professional profiles. Results: In total, 1106 submissions were accepted during the 2019 period, whereas 900 articles (18.6% decrease) were accepted during the same period in 2020. Original research articles comprised 33.3% of the 2019 articles but only 4.9% of the 2020 articles. Female authors contributed to 39.3% (23.1% original research and 16.2% nonoriginal articles) and 29.4% (3.3% original research and 26.1% nonoriginal articles) of articles during the 2019 and 2020 periods, respectively. This represents a marked change in the type of articles that female authors contributed to. Conclusions: Early on during the COVID-19 pandemic, the type of articles accepted, and authorship demographic changed. There was a decrease in contribution of female-authored CT surgery articles submitted to both journals, especially for original research. Future research will elucidate the long-term impact of the pandemic on sex disparities in academic productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".