Reporting Race and Ethnicity In Research Presented at the Society of Gynecologic Surgeons' Annual Meeting
Bibliographic record
Abstract
Objective: Inclusion of minority women in gynecologic research is vital for preventing health care inequities and disparities. This research was conducted to determine how frequently race and ethnicity data were reported in oral presentations at the Society for Gynecologic Surgeons (SGS)'s annual meeting. Materials and Methods: The abstracts and articles associated with SGS oral presentations between 2016 and 2020 were reviewed. Data regarding the numbers of subjects and reported races and ethnicities were extracted from each study. The proportion of studies that reported data about race and ethnicity was calculated. The racial and ethnic distributions of subjects within the studies that reported race and ethnicity were compared to distributions in the U.S. census data. Results: The inclusion criteria were met by 72/92 available abstracts and 28/37 available, articles. Data were reported on participants' race in 10/72 (13.9%) abstracts and 21/28 (75.0%) articles. Ethnicity was reported in 3/72 (4.2%) abstracts and 14/28 (50.0 %) articles. In the abstracts and articles that did report on race, races other than White were underrepresented, compared to the U.S. population. Conclusions: Most research abstracts at SGS annual meetings did not include race or ethnicity data. SGS articles were more likely to report these data but did not represent the the U.S. population diversity accurately. (J GYNECOL SURG 38:241)
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 | 0.071 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".