Engaging target users to appraise and refine clinical practice guidelines in pediatric concussion: An integrated knowledge translation approach
Bibliographic record
Abstract
Background Clinical practice guidelines are systematically developed statements that assist clinicians in making evidence informed decisions regarding patient care. Within pediatric concussion, the Ontario Neurotrauma Foundation released the Guidelines for Diagnosing and Managing Pediatric Concussion in 2014. The purpose of this study was to evaluate the 2014 guidelines using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) evaluation tool, in addition to a brief knowledge translation survey, and to utilize the collected feedback from end users to inform improvements to support an updated version. An integrated knowledge translation approach was employed using clinical experts as guideline appraisers. Methods A purposive sample of researchers, physicians, allied health professionals, policy makers, educators and knowledge translation experts involved in updating the guidelines (N = 31) completed the AGREE II Likert scale survey regarding the 2014 guideline, and provided written justifications for their ratings. Domain and item AGREE II scaled scores were reported stratified by demographic factors, and written justifications were synthesized using content analysis to determine areas of improvement for the 2014 guideline. Results Appraisers scored the editorial independence (88.9%) and scope and purpose (80.8%) domains the highest, indicating high quality. The guidelines scored the lowest in the applicability domain (69.3%). Participants with less than 10 years of experience in their respective disciplines, as well as physicians and allied health professionals consistently provided higher ratings across domains compared to other professions. Conclusions The process of evaluating the 2014 guideline resulted in these important outcomes: (1) identified areas of the guideline that may have affected the lack of previous clinical uptake while abiding by a clinical practice guideline development framework; (2) shared and informed decision making regarding content and format of the revised clinical practice guideline; and (3) targeted content, clinical questions and dissemination strategies, which are key to clinical uptake.
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 | 0.237 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".