IMPROVING CLINICAL PRACTICE GUIDELINES FOR THE 21<scp>ST</scp> CENTURY
Bibliographic record
Abstract
Through the use of three scenarios, this paper presents the challenges for clinical practice guidelines in the 21st century. Such challenges relate to technological developments to improve the efficiency and pace of the development process, to ensure that clinical practice guidelines are kept up to date, and to facilitate implementation of guidelines in the clinical setting. To improve and ensure the validity of the content of clinical practice guidelines, we need to address the important problem of publication bias, for which researchers, granting agencies, industry, and journal editors share responsibility. This means insisting on registration of trials at their inception, and incentives backed up by rules for funding and peer review publication that would promote behaviors to avoid publication bias. The more difficult challenges for clinical practice guidelines relate to what are referred to as attitudinal factors. To achieve optimal efficiencies in development and maintenance of clinical practice guidelines, we need to promote cooperation among various information resource providers internationally and to stress partnership over leadership. Finally, there need to be reconciliation of the different stakeholder perspectives of the value and purpose of clinical practice guidelines so that they are used appropriately as aids to decision making and are not abused as tools for controlling clinical practice.
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 | 0.250 | 0.439 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.026 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".