Mass Screening for Chronic Kidney Disease in Rural and Remote Canadian First Nations People: Methodology and Demographic Characteristics
Bibliographic record
Abstract
BACKGROUND: Screening the general population for Chronic Kidney Disease is not currently recommended.. Rural and remote Canadian First Nations people suffer a disproportionate burden of Kidney Failure. The Fi rst N at i ons Community Based S creening to Improve Kidney He alth and Prevent D ialysis ( FINISHED ) project intends to test the hypothesis that a mobile, mass screening initiative available to all First Nations people 10 years of age and older residing in rural and/or remote communities, is feasible, will improve health outcomes and is cost effective. OBJECTIVES: The objective of this manuscript is to describe the key elements required to design, implement and evaluate such a program and describe key characteristics of our screened cohort. DESIGN: Methods and cohort description. SETTING: 11 First Nations communities within 2 Tribal Councils in Manitoba, Canada. PATIENTS: All First Nations individuals between the ages of 10-80 living in the 11communities were eligible for the screening initiative. MEASUREMENTS: Screening Rates achieved within communities. METHODS: An interdisciplinary team partnership was established between the Diabetes Integration Project and the Manitoba Renal Program. Stakeholder consultation was obtained and protocols developed to mass screen community members using point of care testing equipment. All people screened were risk stratified, counselled and referred to nephrologists as required in real time, based on risk. RESULTS: As of August 31, 2014, 1480 people in 11 communities over 2 Tribal Councils have been successfully screened. A mean screening rate of 21% of all community members eligible (aged 10-80) has been achieved. All patients at intermediate or high risk of kidney failure have been seen by nephrologists within 1 month of screening. LIMITATIONS: Long term outcomes of kidney failure rates not assessed for at least 5 years. Alternative public health initiatives to reduce kidney failure not investigated. CONCLUSIONS: Point of care mass screening, real time risk prediction and counselling of First Nations people at high risk of Kidney Failure is feasible in rural and remote communities. Further analysis of this cohort will describe theepidemiology of CKD in these communities, and test the cost effectiveness of this strategy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".