Incidence of chronic kidney disease in patients undergoing arthroplasty: A systematic review of the literature
Bibliographic record
Abstract
Patients undergoing arthroplasty are exposed to different interventions that can lead to renal dysfunction. There is abundant evidence of the incidence and factors associated with acute kidney injury (AKI); however, the incidence and the factors associated with chronic kidney disease (CKD) are not clear. The objective of this study is to determine the incidence and associated factors in arthroplasty patients. A systematic review of the literature was carried out following the recommendations of PRISMA and the Cochrane Collaboration (PROSPERO Protocol CRD42018075929). The search was carried out in Medline, Embase, Cochrane and LILACS. No language or date limits were set. Observational studies were included: cases and controls, and cohorts. The revision of titles and abstracts and the reading of the full texts was performed in a paired manner. The quality of the evidence was evaluated with the Newcastle-Ottawa tool. The initial search found 1279 titles and abstracts. We excluded 115 duplicates, and 1153 in the reading of titles and abstracts. Three articles met the inclusion criteria and were of acceptable quality. The incidence of severe CKD after hip or knee arthroplasty was 1.2% at 1 year up to 6.5% at 9 years. The evidence of the incidence and risk factors associated with CKD in patients undergoing arthroplasty is very scarce and heterogeneous. Further primary studies are required in order to have more valid and trustable results.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".