Antiphospholipid antibodies in <scp>end‐stage</scp> renal disease: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
Introduction The relationship between autoimmune hemolytic anemia and antiphospholipid antibodies (aPL) and/or antiphospholipid syndrome has never been systematically addressed. Methods Systematic review of EMBASE and PubMed databases performed according to PRISMA guidelines from inception to March 2020; meta‐analysis performed by Peto's odds ratio for rare events. Findings Forty‐five studies with different outcomes met the inclusion/exclusion criteria. The pooled prevalence (PP) of IgG anticardiolipin antibodies (aCL) positivity was greater in end‐stage renal disease (ESRD) than controls (20.2% vs. 2.6%, P = 0.001, I2 >80%; I2 = heterogeneity), particularly in hemodialysis patients (18.3% vs. 8%, I2 = 0%). The PP of lupus anticoagulant was greater in ESRD than controls (8.7% vs. 0.2%, P < 0.0001, I2 = 0%). The standardized mean difference of IgG aCL favored ESRD rather than controls (P < 0.0001, I2 =97%). The PP of fistula occlusion was greater in IgG aCL‐positive patients than negative patients (39% vs. 27%, I2 =97%); the PP of IgG aCL positivity was greater in patients with fistula occlusion than without fistula occlusion (26.9% vs. 23.2%, P = 0.01, I2 =72%); the same applied to the PP of lupus anticoagulant positivity (23% vs. 0.3%, P < 0.0001, I2 = 0%). The standardized mean difference of IgG aCL favored fistula occlusion (P = 0.004, I2 = 91%). Discussion Lupus anticoagulant relates to ESRD regardless of management whereas IgG aCL relates specifically to ESRD on hemodialysis, but only lupus anticoagulant associates with fistula occlusion. The expression of aPL as patients positive for aPL rather than as titers precludes further assumptions on the relationship.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".