Tonsillectomy in a European Cohort of 1,147 Patients with IgA Nephropathy
Bibliographic record
Abstract
BACKGROUND: Tonsillectomy has been considered a treatment for IgA nephropathy (IgAN). It is aimed at removing a source of pathogens, reducing mucosa-associated lymphoid tissue and decreasing polymeric IgA synthesis. However, its beneficial effect is still controversial. In Asia, favorable outcomes have been claimed mostly in association with corticosteroids. In Europe, small, single-center uncontrolled studies have failed to show benefits. METHODS: The European validation study of the Oxford classification of IgAN (VALIGA) collected data from 1,147 patients with IgAN over a follow-up of 4.7 years. We investigated the outcome of progression to end-stage renal disease (ESRD) and/or 50% loss of estimated glomerular filtration rate (eGFR) and the annual loss of eGFR in 61 patients who had had tonsillectomy. RESULTS: Using the propensity score, which is a logistic regression model, we paired 41 patients with tonsillectomy and 41 without tonsillectomy with similar risk of progression (gender, age, race, mean blood pressure, proteinuria, eGFR at renal biopsy, previous treatments and Oxford MEST scores). No significant difference was found in the outcome. Moreover, we performed an additional propensity score pairing 17 patients who underwent tonsillectomy after the diagnosis of IgAN and 51 without tonsillectomy with similar risk of progression at renal biopsy and subsequent treatments. No significant difference was found in changes in proteinuria, or in the renal end point of 50% reduction in GFR and/or ESRD, or in the annual loss of eGFR. CONCLUSION: In the large VALIGA cohort of European subjects with IgAN, no significant correlation was found between tonsillectomy and renal function decline.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".