Bibliographic record
Abstract
BACKGROUND: The transfer of evidence from research into clinical practice is made almost impossible by enormous volume of literature on any topic. Consolidated evidence into guidelines is not very helpful as there are usually 50 guidelines existing on common clinical topics. Clinicians need assistance in identifying the best available evidence. This paper describes two strategies to transfer research evidence into clinical practice. METHODS: The Guideline Advisory Committee (GAC) in Ontario has assessed all available guidelines on 70 clinical topics using a validated and transparent process involving community-based physicians as assessors. A single best guideline is selected and a summary of its evidence-based recommendations are produced for easy use by practitioners (http://www.gacguidelines.ca). The Critically Appraised Practice Reflection Exercise (CAPRE) programme takes the best available evidence on 40 common practice problems, presents a summary for clinician and patient, has a strategy for physician and patient to find common ground in applying the evidence and has the practitioner to carry out a reflection exercise to gain continuing education credits (http://www.capre.ca). Distribution of these strategies in practice-based research networks is a further step in making research more relevant to practice. RESULTS: The GAC website has more than 100,000 'hits' per month and 4500 identified regular users from Canada and the world. The numbers are steadily increasing. The CAPRE programme has not been formally evaluated but over 150 clinicians have used the programme with patients. With a national launch, the programme there between 60,000 and 80,000 hits per week with 100 physicians completing the programme for continuing medical education (CME) credits in the first month. Physicians report that their patients are very pleased with their physician using the latest evidence to address their problem. This is true even if the patient does not agree to follow the evidence-based recommendations. Using these programmes in practice-based research, networks should further promote making research more relevant to practice. CONCLUSIONS: Transferring research-based evidence into clinical practice has many challenges. Two programmes developed to address these challenges are described. Although not fully evaluated, there is some evidence of success.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.005 |
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 teacher head, 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".