QUALITY OF PRIMARY CARE FOR CHRONIC KIDNEY DISEASE
Bibliographic record
Abstract
Background: International guidelines provide recommendations for early chronic kidney disease care. This thesis was completed to 1) measure the quality of chronic kidney disease care and identify gaps, 2) identify reasons why patients do not receive recommended care, and 3) determine if these guideline-recommended practices are associated with better patient outcomes. Methods: Population-based cohort studies were conducted for studies 1, 3 and 4. Using consensus-based indicators, study 1 quantified the quality of care for patients with early chronic kidney disease. Study 2 was a qualitative descriptive study eliciting primary care physicians’ perceived enablers and barriers to follow-up laboratory testing to confirm chronic kidney disease. Study 3 assessed the association between non-steroidal anti-inflammatory drug (NSAID) use versus non-use and adverse clinical outcomes among older adults. Study 4 assessed whether routine serum creatinine and potassium monitoring (versus no monitoring) following angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) initiation among older adults associated with better outcomes. Results: In study 1, most recommendations were being followed; however, some care gaps were identified. For example, half of the patients with initial abnormal kidney test results did not receive follow-up tests. This finding prompted study 2, where enablers and barriers to this practice were identified. Providers were aware that they should be ordering follow-up tests and had the resources to do so. However, some providers perceived this practice as low priority. In study 3, NSAID use was associated with a higher risk of complications. In study 4, routine ACEi / ARB monitoring did not prevent adverse outcomes. Conclusions: This thesis provides a better understanding of care gaps for patients with early chronic kidney disease in Ontario, and reasons for one of these care gaps. This research also provides evidence to help strengthen guideline recommendations (NSAID avoidance) or refute them (ACEi / ARB monitoring).
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".