Bibliographic record
Abstract
Clinical practice guidelines (CPGs) can promote beneficial practices and reduce the likelihood of medical harm [1]. When applied to chronic disease management models, such as for the awareness, treatment and control of hypertension, CPGs have led to improvements in controlling the condition, while reducing the risk of cardiovascular and cerebrovascular death [2]. However, developing good CPGs alone will not lead to beneficial outcomes. Having healthcare providers change their behaviors to use CPGs requires ongoing active dissemination, effective implementation, and resources typically greater in magnitude than those required to create the guidelines [3]. Designing effective implementation strategies requires an understanding of the practice environment, in addition to the behavioral characteristics and needs of the end user. It is also important to recognize that many individuals within the primary care community distrust guidelines owing to the perception that there are already too many contradictory and cumbersome guidelines that have not all been created equally or with the same degree of quality [4]. This chapter is divided into three sections that correspond to inter-related domains regarding the use of CPGs in evidence-based medicine. The first section examines the process and substantive elements that go into the development of CPGs, along with methodology and tools for CPG development. The second section looks at guideline use in practice and the newly emerging area of evidence-based implementation along with some of the current evidence on effective strategies. The last section discusses some of the challenges of evaluating the successful use of guidelines in clinical practice. The Canadian Hypertension Education Program (CHEP) is used as an example of a highly effective guidelines program.
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 | 0.026 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.142 | 0.111 |
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".