Practice Guidelines Do Improve Patient Outcomes: Association or Causation?
Bibliographic record
Abstract
Guidelines have been developed in nephrology and medicine developed to assist practitioners and patients in making decisions about healthcare for specific clinical circumstances. There has been a proliferation of guidelines over the last decade in all areas of medicine, including nephrology. Many of the nephrology guidelines are based on a less robust evidentiary base than guidelines in cardiology or diabetes. There continues to be a debate in medicine as to whether guidelines and their development process actually impact patient outcomes. This article describes the ways in which guidelines may impact patient outcomes in nephrology and emphasizes the role of guidelines in education, research and health policy development such that there is an indirect benefit on medical practice and thus patient outcomes. Our failure to be able to directly attribute any specific guideline to a change in patient outcomes speaks to the complexity of CKD patients, and the difficult in measuring hard outcomes versus process outcomes. Examples of the activities stimulated by guidelines in key areas of nephrology are given. Guidelines are an important component of the application of medical knowledge to medical practice, and need to be contextualized as such. Rigorous evaluation of current implementation techniques and resultant impacts should be undertaken.
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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".