Qualitative and Quantitative Dosage of the Anti M-Type Phospholipase A2 Receptor Autoantibody: One-Year Experience in Quebec's Reference Center
Bibliographic record
Abstract
Background: Dosage of the M-type phospholipase A2 receptor antibodies (anti-PLA2R) is now an essential tool for diagnosis and management of primary membranous nephropathy (MN). Since October 2018, Hôpital Maisonneuve-Rosemont (HMR) has been designated as Quebec's reference center for serum anti-PLA2R antibody testing by the Institut National d'Excellence en Santé et Services Sociaux (INESSS), the regulatory body on drugs and tests usage in Quebec. Methods: All patients who had a serum anti-PLA2R antibody testing performed at HMR from October 1st, 2018 to October 1st, 2019 were included in the study. Serum anti-PLA2R antibodies were screened by a qualitative test, followed by a quantitative test if the results were undetermined or positive. We calculated sensitivity, specificity, predictive value, and likelihood ratio for both tests, using kidney biopsy findings as the gold standard. Results: In the province of Quebec, a total of 1690 tests were performed among 1025 patients during the study year. A small proportion of these patients (8%) were followed at HMR. Patients tested at HMR and in the rest of Quebec had similar characteristics. Test validity was only characterized for patients tested at HMR. Sensitivity and specificity were, respectively, 58% and 100% for the qualitative test, and 71% and 100% for the quantitative test. The combined net sensitivity was 42% and the net specificity, 100%. The net positive and negative predictive value were 100% and 84% respectively, whereas the net negative likelihood ratio was 0.58. Conclusions: Serum anti-PLA2R antibody testing was widely used in Quebec during its first year of availability. In one of the biggest real life cohort described, the test performed as previously described in the literatur. Moreover, the two-step approach that was used at HMR, using a qualitative test before a quantitative test if needed, appears to be an efficient way to avoid quantitative testing in negative patients and to better characterize undetermined results on immunofluorescence. Funding: Government Support - Non-U.S.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".