The Development, Problems and Application of Clinical Practice Guidelines: How Clinicians Can Read the Essence of Clinical Guidelines from the Surface to the Inside
Bibliographic record
Abstract
Evidence-based medicine has greatly contributed to the improvement of the level of medical services and health regulation. However, the critical use of the published medical literature and medical databases by Chinese clinical practitioners is insufficient, and the achievements of cutting-edge research methods of evidence-based medicine are not used by every Chinese clinical practitioner for benefit. This article provided a concise and well understood methodological illustration of the development of evidence-based medicine practice guidelines, to help Chinese clinical practitioners better understand the philosophy of guideline development by presenting the definitions of clinical practice guidelines and sorting out the standard development process of the guidelines. This paper analyzes from three parts, "Formulation of Clinical Practice Guidelines""Problems Existing in the Formulation of Clinical Practice Guidelines" and "How Clinicians Apply Clinical Guidelines". In this paper, the two guideline development standards, published by World Health Organization (WHO) and together with Guideline International Network (GIN) and McMaster University, were simplified, combined and summarized into were more concise. The problems existing in the formulation of clinical practice guidelines are analyzed with cases, in order to guide clinicians to apply the guidelines.
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 | 0.300 | 0.550 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.029 | 0.040 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.021 | 0.035 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".