Kidney Check : Identifying Kidney Disease and Diabetes in Bc First Nations Communities
Bibliographic record
Abstract
BackgroundKidney disease has a strong impact on the health and wellness of Indigenous communities in Canada. Therefore, a national strategy to improve kidney health must include meaningful, culturally appropriate engagement with Indigenous peoples. The Can-SOLVE CKD Network is a pan-Canadian patient-oriented kidney research initiative that is working to improve the health of all Canadians and bring Indigenous ways of knowing into health research.MethodsThe Can-SOLVE CKD Network is working with BC Renal and the First Nations Health Authority to develop and implement a new program that will bring kidney, diabetes, and blood pressure checks to First Nations communities. Kidney Check is a screening, triage, and treatment program using point-of-care testing and trained health care teams. Each participating community has the opportunity to design and work with the Can-SOLVE CKD team to develop a locally acceptable program, which helps to identify healthy kidneys as well as those with mild, moderate or severe kidney problems. The results will be shared with participants in real time. Each person tested will also participate in building their own kidney health plan, including follow-up goals for maintaining kidney health.ResultsTen BC communities have been chosen through a transparent process to be part of phase 1 of the program, which is launching in Fall 2019. The ultimate aim is to roll out Kidney Check to all Indigenous communities in BC. Kidney Check programs are also under development in Alberta and Manitoba.ConclusionThe Kidney Check program aims to help keep kidneys healthy and is working in partnership with First Nations communities to do so.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".