Gender disparities in multiple myeloma publications.
Bibliographic record
Abstract
e23000 Background: Gender disparities exist in academia and are disproportionately affecting females. We conducted a cross sectional study to analyze gender disparities in MM publications in high impact hematology/oncology journals between January 1st, 2016 and December 31st, 2020. Methods: Data was collected through Medline/PubMed database search. High impact journals (impact factor > 10) with at least ten MM related publications were included. Impact factor data was based on the 2016 InCites Journal Citation Report. We used Genderize, a validated database, to determine authors’ gender. Search engines were used if gender was undetermined or did not meet a cut-off of 0.9. Unclassified genders after search were excluded from analysis. Results: A total of 679 publications with 8898 authorships were analyzed. Mean number of authors for females vs. males, per publication was 4.4 and 8.7, respectively. Females constituted a third of total authors. Female first authors, corresponding authors and last/senior authors were 34%, 21% and 18%, respectively. 17% of authors of clinical trial publications were females. Publications by country had comparable percentages (30% females as first authors in the U.S. vs. 32% females as first authors in other countries). The proportion of female authorship in 2020 when compared to 2016 was similar (33%). Between 2016 and 2020, there was slight improvement in female first authors (32%-37%), last authors (16%-21%), and corresponding authors (17%-22%). Conclusions: Gender disparities in MM publications exist and are more obvious in the last/corresponding authorship in both the U.S. and other countries. Despite improvement in proportion of female authors, it remains suboptimal when compared to the growing proportion of female physicians. Efforts should be made to identify factors that contribute to these disparities and work to resolve them.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.027 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".