Bibliographic record
Abstract
Summary In order to establish or maintain a high level of patient care, design credible clinical policy and contribute to an overall quality mandate, evidence‐based guidance is needed on many topics. In this chapter, clinical practice guidelines (CPG) and clinical standards will be described along with the similarities and differences of these tools. Next, readers will be presented with an overview of the concept, levels of evidence and the quality of reporting of any included study. Readers will learn about the role of this concept in the development of clinical practice guidelines and the implications for recommendations. The chapter will conclude with an in‐depth discussion of the components of a high‐quality CPG. Readers will have a firm grasp of the basic concepts associated with CPG development and what comprises a high‐quality document.
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 | 0.037 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.083 | 0.070 |
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".