Sex Differences in the Recognition, Monitoring, and Management of CKD in Health Care: An Observational Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Reported sex differences in the etiology, population prevalence, progression rates, and health outcomes of people with CKD may be explained by differences in health care. METHODS: measurement denoting probable CKD in Stockholm from 2009 to 2017. We calculated the odds ratios for diagnosis of CKD and provision of RASi and statins at inclusion, and hazard ratios for CKD diagnosis, visiting a nephrologist, or monitoring creatinine and albuminuria during follow-up. RESULTS: We identified 227,847 subjects, of whom 126,289 were women (55%). At inclusion, women had lower odds of having received a diagnostic code for CKD and were less likely to have received RASi and statins, despite having guideline-recommended indications. In time-to-event analyses, women were less likely to have received a CKD diagnosis (HR, 0.43; 95% CI, 0.42 to 0.45) and visited a nephrologist (HR, 0.46; 95% CI, 0.43 to 0.48) regardless of disease severity, presence of albuminuria, or criteria for referral. Women were also less likely to undergo monitoring of creatinine or albuminuria, including those with diabetes or hypertension. These differences remained after adjustment for comorbidities, albuminuria, and highest educational achievement, and among subjects with confirmed CKD at retesting. Although in absolute terms all nephrology-care indicators gradually improved over time, the observed sex gap persisted. CONCLUSIONS: There were profound sex differences in the detection, recognition, monitoring, referrals, and management of CKD. The disparity was also observed in people at high risk and among those who had guideline-recommended indications. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/JASN/2022_10_11_JASN2022030373.mp3.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".