Sociodemographic associations with abnormal estimated glomerular filtration rate (eGFR) in a large Canadian city: a cross-sectional observation study
Bibliographic record
Abstract
BACKGROUND: . Certain sociodemographic groups are known to be at risk for CKD, but it is unclear if there are strong associations between these at risk groups with abnormal eGFR test results in Canada. Using only secondary laboratory and Census data, geospatial variation and sociodemographic associations with abnormal eGFR result rate were investigated in Calgary, Alberta. METHODS: Secondary laboratory data from all adult community patients who received an eGFR test result were collected from Calgary Laboratory Service's Laboratory Information System, which is the sole supplier of laboratory services for the large metropolitan city. Group-level sociodemographic variables were inferred by combining laboratory data with the 2011 Canadian Census data. Poisson regression and relative risk (RR) were used to calculate associations between sociodemographic variables with abnormal eGFR. Geographical distribution of abnormal eGFR result rates were analyzed by geospatial analysis using ArcGIS. RESULTS: ). Geospatial analysis revealed distinct geographical variation in abnormal eGFR result rates in Calgary. Women (RR = 1.11, P < 0.0001), and the elderly (age ≥ 70 years; P < 0.0001) were significantly associated with an increased risk for CKD, while visible minority Chinese (RR = 0.73, P = 0.0011), South Asians (RR = 0.67, P < 0.0001) and those with a high median household income (RR = 0.88, P < 0.0001) had a significantly reduced risk for CKD. CONCLUSIONS: Presented here are significant sociodemographic risk associations, and geospatial clustering of abnormal eGFR result rates in a large metropolitan Canadian city. Using solely publically available secondary laboratory and Census data, the results from this study aligns with known sociodemographic risk factors for CKD, as certain sociodemographic variables were at a higher risk for having an abnormal eGFR test result, while others were protective in this analysis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".