Performance of the Modification of Diet in Renal Disease and Cockcroft-Gault Equations in the Estimation of GFR in Health and in Chronic Kidney Disease
Bibliographic record
Abstract
<h3>Background:</h3> Gender disparities in faculty rank have yet to be studied among Canadian physicians. The purpose of this study was to determine whether differences in region, training, research productivity and years in practice explain gender differences in academic promotion among Canadian general surgeons. <h3>Methods:</h3> We developed a cross-sectional database of faculty-appointed general surgeons practising in the hospitals affiliated with the 17 universities within the Association of Faculties of Medicine of Canada in 2017 using publicly available directories, university and hospital websites, and direct communication. The data were collected between October and December 2018 and included gender, residency completion year, graduate education, fellowships, number of publications and Scopus h-index; faculty lists and professorship status were verified by program administrators or division heads of their respective divisions. The dependent variable was binary: full professor or not. A combined outcome of associate or full professor was also analyzed. We analyzed all variables in a multivariable logistic regression model. <h3>Results:</h3> Of the 17 institutions contacted, all but 1 confirmed the faculty lists and professorship status. A total of 405 surgeons were included, of whom 111 (27.4%) were women. Sixty-eight women (61.3%) and 120 men (40.8%) were assistant professors, and 9 women (8.1%) and 75 men (25.5%) were full professors. Although on average women had completed residency more recently than men (15.2 yr v. 19.2 yr, <i>p</i> < 0.001), there was no difference between men and women in the mean number of publications as residents (2.98 v. 2.74, <i>p</i> = 0.7) or per year of practice (3.12 v. 2.09, <i>p</i> = 0.2), number of fellowships pursued (<i>p</i> = 0.7) or graduate education (<i>p</i> = 0.2). In the multivariable model (C-statistic = 0.88), gender remained significantly associated with full professorship (odds ratio 2.79, 95% confidence interval 1.13 to 6.92), along with years in practice (odds ratio 1.61, 95% confidence interval 1.13 to 2.30). <h3>Interpretation:</h3> After controlling for years in practice, training and research productivity measures, we found that female surgeons with faculty appointments in Canada were less likely than their male counterparts to receive promotion to full professor. Pervasive inequities in systems of promotion must be addressed.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".