Implementing learning health systems in the nephrology program to enhance value-based healthcare delivery
Bibliographic record
Abstract
The Health Accord for Newfoundland and Labrador recommended the creation of learning health system (LHS) to improve healthcare delivery in our province. The Nephrology Program identified two opportunities to collect data to implement improved care consistent with a local LHS: (1) decreased time on in-centre hemodialysis (ICHD) and (2) increased uptake of home dialysis. First, a direct method for calculating urea distribution volume in ICHD patients was compared to the current method of monitoring dialysis dose. The two volumes were used to calculate independent dialysis doses, Kt/V. The mean Kt/V difference was significantly higher for the direct method, a difference that was greater in obese patients and amputees. This suggests current methods overestimate volume, underestimate Kt/V, and lead to prescriptions for increased dialysis time. Second, metrics of newly started dialysis patients were analyzed for differences between ICHD and home dialysis patients and their care, to identify barriers to transitioning to home modalities. Five barriers were identified, including less pre-dialysis staff exposure and lower rates of discussions of home dialysis options. These data can be used to implement Nephrology Program LHS cycles in ongoing quality improvement initiatives, with multifaceted outcome goals in healthcare delivery, including patient care experience, population health, and health care delivery cost.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".