What does it take to facilitate the integration of clinical practice guidelines for the management of low back pain into practice? Part 1: A synthesis of recommendation
Bibliographic record
Abstract
BACKGROUND: Despite the emergence of multiple clinical practice guidelines (CPGs) for the rehabilitation of low back pain (LBP) over the last decade, self-reported levels of disability in this population have not improved. This may be explained by the numerous implementation barriers, such as the complexity of information and sheer volumes of CPGs. OBJECTIVES: The purpose of this study was to summarize the evidence and recommendations from the most recent and high-quality CPGs on the rehabilitation management of LBP by developing an infographic summarizing the recommendations to facilitate dissemination into clinical practice. METHODS: We performed a systematic review of high-quality CPGs with an emphasis on rehabilitation approaches. We searched major health-related research databases (e.g., PubMed, CINAHL, and PEDro). We performed quality assessment via the AGREE-II instrument. Contents of the CPGs were synthesized by extracting recommendations, which were then compared to one another to identify consistencies based on an iterative evaluation process. RESULTS: We identified and assessed 5 recent high-quality CPGs. We synthesized 13 recommendations on the rehabilitation management of LBP (2 for screening procedures, 3 for assessment procedures, and 8 involving treatment approaches) and 2 underlying principles were highlighted. These results were then synthetized and illustrated in a concise infographic that serves as a conceptual roadmap that identifies the specific behavior changes (i.e., adoption of CPGs' recommendations) rehabilitation professionals should adopt in order to integrate an evidenced-based approach for the management of LBP. CONCLUSIONS: We systematically reviewed the literature for CPGs' recommendations for the physical rehabilitation management of LBP and synthesized the information through an infographic.
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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 | 0.232 | 0.507 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.027 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".