Guidelines and Algorithms: Perceptions of Why and When They Are Successful and How to Improve Them
Bibliographic record
Abstract
Medicine is increasingly complex, a reality created by the explosion of knowledge during the last 50 years. The cost of applying this knowledge creates a daunting economic challenge. As a result, there has been a profusion of guidelines intended to influence medical practice. This report explores the interrelated issues and concepts that impact the value and success of guidelines. These include medical quality and error, compliance, and the impact on outcomes in an evidence-based medicine context. Lessons learned from previous guidelines must be understood in relation to human behavior. Legal implications of the guidelines must be considered because both an increase and a decrease in liability can be anticipated. Many products have been labeled "advocacy guidelines" with a negative context. They are believed to express motivation rather than optimizing care. The ideal of professionalism is challenged, and there is potential for the growing use of guidelines in enforcing punitive actions. Constructive experience has emphasized the appropriate required elements for practice guidelines: a systematic review of the literature, an assessment of the volume and level of the evidence, and development of a review process by an appropriate multidisciplinary group for consistency, clinical impact, and resource implications leading to clearly stated and reasonable recommendations. The dissemination of guidelines, beyond conventional publication in a journal, will impact the success of the intended outcomes. The exploitation of electronic avenues, including the Internet and the evolving interactive electronic medical record, seems to be essential for future success in these endeavors.
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 | 0.141 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".