MO1037WOMEN REPRESENTATION IN CLINICAL PRACTICE GUIDELINES AMONG MAJOR NEPHROLOGY GUIDELINES
Bibliographic record
Abstract
Abstract Background and Aims While the percentage of women entering nephrology has increased over the years, women representation and sex disparities in the authorship of major nephrology Clinical practice guidelines (CPG) has not been examined. Our study evaluates current sex disparities and women representation, and the nationalities of women authors in nephrology CPGs developed by the Kidney Disease: Improving Global Outcomes (KDIGO), Kidney Disease Outcomes Quality Initiative (KDOQI), and the European Renal Best Practice (ERBP), the official guideline body of the ERA-EDTA. Method We examined the number of female versus male guideline workgroup members (panelists) for all available CPGs in each of the three organizations as of Dec. 2020, which are available on their respective websites. We discerned the sex of the panelists based on google search and their affiliated institutional websites. We obtained the nationalities of the workgroup members from the authorship information of the respective CPG. Results Of the total 488 panelists in all three organizations, 115 (23.6%) were females and 373 (76.4%) males. KDIGO had 184 panelists, of which 46 (25%) were females. The CPGs with the highest and the least women representation are ‘Diabetes in Chronic Kidney Disease (CKD)’ (41.2%) and ‘Anemia in CKD’ (11.8%), respectively. The countries with the highest number of women representations are the USA (20), followed by Canada (6), and China (4). In KDOQI, 39 (31%) of 127 panelists were female. While CPGs related to ‘Evaluation and management of CKD’ and ‘Nutrition in children with CKD’ each had 50% female panelists, ‘Blood pressure management in CKD’ CPG had 10% female panelists. 28 (72%) of the total 39 women were from the USA. The ERBP had 30 (17%) females of the total 177 panelists. ‘CKD in older patients’ CPG comprised 42.1% female panelists. CPGs for ‘Glycemic control in diabetes’ and ‘Glucose lowering drugs in diabetes’ had no female panelists. Belgium and UK each had six women representatives, while France and The Netherlands 4 women representatives each. Conclusion The guidelines developed by the most prominent organizations – KDIGO, KDOQI, and ERA-EDTA have less than 25% women representation. While it is encouraging to note that there is more women representation in some of the CPGs developed by KDIGO in 2020, evaluating the barriers contributing to the under-representation of women in major nephrology organizations is warranted. Also notable is a lack of women representation from developing countries.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".