Geospatial Mapping to Identify Primary Glomerulonephritis Hot Spots in Saskatchewan
Bibliographic record
Abstract
Background: Saskatchewan (SK), a province rich in oil, minerals, and agriculture, with 1.2M population, is principally served by its two urban renal centers in Regina and Saskatoon. There is a lack of comprehensive data on the association of GN subtypes with rural-urban divide, and geographic hotspots like mines and refineries.Utilizing the kidney biopsy, our objective was to identify clusters of GN subtypes, calculate yearly incidence rates, urban/rural comparisons, and distance and travel time to access care. Methods: A centralized provincial kidney pathology database was used to capture all incident cases of biopsy-proven GN in the adult population from 2002 - 2018. Only patients with primary GN were included (n=1372). Demographic attributes, 3-digit postal codes, lab parameters, definitive GN diagnosis, the date of biopsy, dialysis start, and death were collected and analyzed. To analyze the variation of GN across different regions in SK we used SaTScan v10.1.3 software. The population data from Census 2016 was used as a reference, and age and sex were taken as covariates. Results: GN incidence increased from 4.6 to 13.6 per 100,000 persons from 2002 to 2018 (p<0.001). GN incidence was higher in rural areas than in urban areas (p<0.001). Significantly higher dialysis progression rates were seen for rural and remote areas (p<0.01). We identified a geospatial cluster of 345.7 km2 for lupus nephropathy (Figure 1(f)), with an incident rate ratio of 1.73, a relative risk of 2.7, and a Log likelihood ratio of 13.87. No significant clusters were identified for any other subtypes of GN. Conclusion: We identified a geographic cluster for lupus nephropathy, encompassing both urban and rural areas. While there was a higher incidence of MN and AGBM in rural areas, we did not identify any geographic clusters for the same. Addressing and understanding the multifaceted factors driving these disparities are essential steps towards easing the burden of GN on impacted communities.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".