Creating clinically relevant knowledge from systematic reviews: the challenges of knowledge translation
Bibliographic record
Abstract
RATIONALE AND OBJECTIVE: A research translation strategy for chronic pain was developed that has significant potential to advance the usefulness of systematic reviews (SRs) in clinical practice. METHOD: The strategy used interactive case-based workshops that summarize current evidence on treatments for chronic non-cancer pain. Health technology assessment researchers and clinicians collaborated to translate SR evidence into education aids, but this proved far from straightforward. RESULTS: Sourcing and selecting the SR evidence required maintaining a credible balance between the diametrical concepts of comprehensiveness and efficiency, and relevance and validity. On examination of the collated evidence base, further challenges were encountered in dealing with the lack of consistency among the SRs in the quality of execution, the scales used to rate the quality of the evidence, and the conclusions on common topic areas. Strategies for overcoming these difficulties are discussed. CONCLUSIONS: The key elements for creating clinically relevant knowledge from SRs are: a flexible, consistent and transparent methodology; credible research; involvement of renowned content experts to translate the evidence into clinically meaningful guidance; and an open, trusting relationship among all contributors.
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 | 0.870 | 0.893 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".