Assessment of the Siksika chronic disease nephropathy-prevention clinic.
Bibliographic record
Abstract
OBJECTIVE: To determine if a community-based multifactorial intervention clinic led by a nurse practitioner would improve management of First Nations people at risk of developing chronic kidney disease. DESIGN: Qualitative descriptive study. SETTING: A nephropathy-prevention clinic in Siksika Nation, Alta. PARTICIPANTS: First Nations people with diabetes, hypertension, or dyslipidemia who were referred to the clinic. MAIN OUTCOME MEASURES: Changes in blood pressure (BP), hemoglobin A(1c), and low-density lipoprotein levels, as well as in use of antiplatelet therapy, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker medications, and statin therapy. RESULTS: Members of the Siksika Nation were treated according to clinical practice guidelines. A total of 78 patients had at least 2 visits to the clinic and were included in this analysis (61.5% were women; mean age 56 years). Among those initially above target, a significant reduction was achieved in mean hemoglobin A(1c) (0.96%; P < .01), systolic BP (15.84 mm Hg; P < .05), diastolic BP (7.16 mm Hg; P < .001), and low-density lipoprotein (0.62 mmol/L; P < .01) levels. There was a significant increase in the proportion of patients with clinical indications who were treated with acetylsalicylic acid (42.4%; P < .01), angiotensin-converting enzyme inhibitor or angiotensin receptor blocker medications (35.9%; P < .01), or statin therapy (35.9%; P < .01). CONCLUSION: A community-based, nurse practitioner-led clinic can improve many clinically relevant factors in patients at risk of developing chronic kidney disease. Studies have shown that achieving treatment targets is associated with a reduced risk of early death and cardiovascular events; the effect in the First Nations population on these hard clinical end points remains to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".