Case Series of Infection-Related Glomerulonephritis in Quebec Indigenous Peoples
Bibliographic record
Abstract
Rationale: Infection-related glomerulonephritis (IRGN) is an immune-mediated glomerulonephritis caused by extra-renal infectious diseases. There has been an important shift in epidemiology in recent years, with a significant proportion of adults affected. The incidence of IRGN is higher amongst Indigenous populations and especially in those with multiple comorbidities. Beginning in 2019, we observed several IRGN cases amongst adult Indigenous peoples referred to the McGill University Health Center (MUHC). The aim of this article is to describe the demographic, clinical, and outcome data of these individuals and highlight the heterogeneity of IRGN in this population through 2 illustrative cases. Presenting concerns of the patient: In total, 8 cases of IRGN were identified between 2019 and 2022. All patients presented with features of acute glomerulonephritis. Diagnoses: All patients had documented evidence of an infection that preceded their diagnosis of IRGN. IRGN was not the initial clinical diagnosis in all cases. Interventions: Half the patients received immunosuppression while the others received supportive care only. Outcomes: Four patients required initiation of hemodialysis at time of presentation and at 2 years of follow-up, 3 of the 4 remained hemodialysis-dependent. Teaching points: Our case series emphasizes the heterogenous clinical, laboratory, and pathological presentations that make the diagnosis of IRGN quite challenging. A high index of suspicion should be present when a patient presents with acute kidney injury, features of a glomerulonephritis, and an infection, especially those with multiple comorbidities and a preceding history of chronic kidney disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".