Prevalence and determinants of albuminuria on a Manitoba First Nation
Bibliographic record
Abstract
Both diabetic and non-diabetic end stage renal disease is more common in First Nations people than those in the general population in Canada.A marker of early potentially preventable disease is abnormal urinary albumin creatinine ratio (ACR).A cross sectional study of the prevalence and determinants of albuminuria was carried out in 4g6 residents of a Plains Ojibwa First Nation in Manitoba in the summer of 2003.Twenty percent of the individuals in the study had albuminuri a. (5.3Vo proteinuria, dipstick protein>lg or ACR >3Omg/mmol,14.77omicroalbuminuria, MAU, ACR>2 men or )2.g female and <30mg/mmot) Those with albuminuria were older (42 yearsvs 36 p<0.0001), male (60Vo vs 45, p=0.01) non smokers (ZgTo vs 14, p=0.01), had more diabetes (60Vo vs 21, p<0.0001), higher fasring glucose (r0 mmol/L vs 6, p<0.0001), higher systoric (134 mmHg vs 125, p<0.0001) and diastolic blood pressures (g0 mmHg vs 76,p=0.001)despite increased use of antihypertensives(32vo vs 12 p<0.0001)or ACEI (22vo vs 6 p <0'0001) than the non albuminuric population.only 7.57o of those with albuminuria were aware of kidney disease.In a multiv anate logistic regression performed in a backwards conditional fashion, independent risk factors for albuminuria were; female gender IOR 0.407 (957o CI0.23,0.7]l),p=0.0021, average fasring glucose IOR I .Z (95Vo cI 1.115, r.29),p <0.00011, years diagnosed with diaberes toR 1.057 (95vo crl.004, 1.113), p=0.031, systolic BP IOR I.0Z (95Vo CI 1.005, 1.035), p=0.0091, and BMI [OR 1.042 (957o CI1.001, 1.085), p=0.041.The high rate of albuminuria in this FN population suggests a screening and multifaceted intervention and education program in those with hyperlension and diabetes is warranted to halt the progression and development of albuminuria and ultimately ESRD.Kue Young, Sharon Bruce and Claudio Rigatto, whose support and guidance made this thesis a reality.I would like to thank the section of nephrology for allowing me to pursue further studies despite a full time clinical commitment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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".