The Appraisal of Clinical Practice Guidelines for Breast Cancer-Related Lymphedema
Bibliographic record
Abstract
Background: Approaches to screening, assessment, and treatment of breast cancer-related lymphedema (BCRL) vary widely. We evaluated overall quality of clinical practice guidelines (CPGs) for managing BCRL using the Appraisal of Guidelines for REsearch and Evaluation II (AGREE II) tool, and relevance of consensus recommendations for the Canadian health context. Methods and Results: We searched electronic databases, gray literature, national lymphedema frameworks, and expert opinions, to identify lymphedema CPGs, printed/published from January 2013 to October 2021. Using AGREE II, six health care professionals reviewed CPGs for consensus. Domain-specific AGREE II quality consensus scores were required (≥70% for Rigor of Development; ≥ 60% for Stakeholder Involvement and Editorial Independence; and ≥50% for Clarity of Presentation, Applicability, Scope, and Purpose). Results and overall recommendations from the CPGs were summarized and synthesized. Nine CPGs met inclusion criteria for review. Wide variability of evidence-based recommendations, and limited clinical considerations were found. Scope and Purpose, and Clarity of Presentation were adequate in six of nine CPGs; Stakeholder Involvement in seven of nine CPGs; and Editorial Independence in three of nine CPGs. Across all CPGs, Applicability was minimally reported. Only the Queensland Health CPG met quality consensus scores for Rigor and Development; however, the focus was limited to compression therapy. Conclusions: No CPG reviewed could be adopted for the Canadian health context. The proposed Canadian BCRL CPG will focus on stakeholder engagement, methodology, and implementation/evaluation. Using AGREE II allowed for assessment of quality of methods used to develop identified CPGs from other countries before consideration of adoption in a Canadian Context.
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 | 0.341 | 0.654 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.014 | 0.014 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".