Women and kidney disease: reflections on World Kidney Day 2018
Bibliographic record
Abstract
The objective of this study was to describe the management of patients undergoing renal transplantation in 2013 and over the following two years on the basis of healthcare consumption data. The National Health Insurance Information System was used to identify 1876 general scheme beneficiaries undergoing a first isolated renal transplantation (median age: 53 years; men 63%). Overall, 1.2% of patients died during the transplantation hospital stay (> 65 years 3.3%) and 87% of patients had a functional graft at 2 years. Thirty-three percent of patients were readmitted to hospital for 1 day or longer during the first month, 73% the first year and 55% the second year. At least 10% of patients were hospitalised for antirejection treatment during the first quarter after renal transplantation, 16% the first year and 9% the second year. The first year, 32% of patients were hospitalised for renal disease (12% the second year), 14% were hospitalised for cardiovascular disease (9% the second year), 13% for infectious disease (5% the second year) and 2% for a malignant tumour (2% the second year). Almost 80% of patients consulted their general practitioner each year (almost 50% consulted every quarter). During the second year, 83% of patients were taking antihypertensives, 45% lipid-lowering drugs, 26% antidiabetic drugs, 77% tacrolimus, 18% ciclosporin, 88% mycophenolic acid and 69% corticosteroids. This study highlights the important contribution of healthcare consumption data to a better understanding of the modalities of management of renal transplant recipients in France, allowing improvement of this management in line with guidelines.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".