Clinical practice guidelines
Bibliographic record
Abstract
In the era of evidence-based medicine, clinical practice guidelines (CPGs) have become an integral part of many aspects of medical practice. Because practicing neurosurgeons rarely have the time or, in some cases, the methodological expertise, to assess and assimilate the totality of primary research, CPGs can in theory provide a vehicle through which neurosurgeons could more efficiently integrate the most current evidence into patient management. Clinical practice guidelines have been met with some skepticism, however, particularly within the neurosurgical community. Some have expressed concerns that the promise of CPGs has not been matched by the reality. Others who oppose CPGs fear that they hinder the art of medicine, and limit physician and patient autonomy. The purpose of this paper is to provide the practicing neurosurgeon with an up-to-date review of CPGs. The authors discuss some of the complexities and recent advancements in CPG development, appraisal, and publication. An overview of the various systems for grading medical evidence and issuing CPG recommendations, each of which has its advantages and disadvantages, is included, and the current knowledge on the impact of CPGs in 2 important realms, patient care and medicolegal issues, is discussed. The purpose of this review is to provide a balanced, current synopsis of what CPGs are, how they are developed, and what they can and cannot do. The authors hope that this will allow neurosurgeons to make more informed decisions about the many CPGs that will inevitably become an essential component of medical practice in the years to come.
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 | 0.031 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.017 | 0.012 |
| Insufficient payload (model declined to judge) | 0.112 | 0.090 |
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".