Gender and Racial Diversity in Relation to Publication Rates at the Canadian Association of Radiology Annual Scientific Meetings 2016 to 2019
Bibliographic record
Abstract
Purpose: To determine the overall rate of publication of abstracts presented at the 2016 to 2019 Canadian Association of Radiology Annual Scientific Meeting (CAR ASM), with an emphasis on gender and racial diversity. Methods: Abstracts from publicly available past programs were analyzed using PubMed, EMBASE, and Google Scholar for publication status, time to publication (TTP), author affiliation, and journal of publication. Past programs were used to determine the abstract format, abstract category, and the subspecialty and imaging modalities explored. First author demographics were identified using the Namsor software. Results: Four hundred and sixty-two abstract presentations were included in the analysis with an overall conversion rate of 34.63%. Two hundred and ninety-two (63.2%) of the first-authors were male-identified, of which 104 (35.62%) were published. In contrast, 170 (36.8%) were female-identified, of which 56 (32.94%) were published. Additionally, 50.87% first-authors were identified as white, 38.31% asian, 6.06% black, 4.76% latino, and 0.00% indigenous. While diversity was seen in demographics, 60% of publications had a white first-author. The following conversion rates were found: 40.85% white, 30.51% asian, 25% black, and 13.64% latino. In terms of abstract category, radiologist-in-training had the highest conversion rate at 60.71%. The median TTP was 14 months, with an average impact factor of 5.26. Conclusion: Less than half of abstracts at the 2016 to 2019 CAR ASM were published and both gender and racial disparities in relation to conversion rates were identified. Measures to improve publication rates and overall diversity in Radiology are warranted.
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 | 0.025 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.034 | 0.037 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".