Post‐streptococcal glomerulonephritis in Sydney: A 16‐year retrospective review
Bibliographic record
Abstract
AIM: Post-streptococcal glomerulonephritis (PSGN) is a frequent cause of acute nephritis in children. Numerous studies have described PSGN in high-risk populations yet few data describing PSGN in a low-incidence population exist. This study aimed to describe the epidemiology, clinical manifestations, diagnosis, complications and outcomes of PSGN in an urban Australian population. METHODS: A 16-year retrospective review of case notes and laboratory data was conducted at a tertiary Sydney paediatric hospital. RESULTS: Thirty-seven children were treated for PSGN with a mean age of 8.1 years (range 2.6-14.1 years). Twenty-eight subjects (75.7%) had a history of a recent upper respiratory tract or skin infection. Hypertension and/or oedema was present in 29 subjects (78.4%). Streptococcal pharyngitis was identified as the likely source in 17 subjects (45.9%). Skin infections occurred less frequently. Antibodies against streptolysin O, streptokinase or deoxyribonuclease B were elevated when a single titre was measured in 35 subjects (94.6%). Thirty subjects (81.1%) developed renal impairment (median peak creatinine, 95 micromol/L, range 39-880 micromol/L). No correlation was demonstrated between peak creatinine, age, ethnicity, streptococcal titres and serum complement levels. The mean length of admission was 8.2 days. Seven subjects (18.9%) had a complicated course with three subjects requiring dialysis. Only one subject has ongoing renal dysfunction. CONCLUSION: Significant differences are seen in a low-incidence urban Australian population with PSGN when compared with endemic or epidemic disease in high-risk populations. The higher rates of complications that were seen compared with previously studied populations need further clarification.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".