Complementary and integrative medicine mention and recommendations: A systematic review and quality assessment of lung cancer clinical practice guidelines
Bibliographic record
Abstract
BACKGROUND: Complementary and integrative medicine (CIM) use is widely sought by those diagnosed with cancer, with up to 50% of lung cancer patients seeking these therapies in the United States. The purpose of this study was to identify the quantity and assess the quality of CIM recommendations in clinical practice guidelines (CPGs) for the treatment and/or management of lung cancer. METHODS: A systematic review was conducted to identify lung cancer CPGs. MEDLINE, EMBASE and CINAHL were searched from 2008 to 2018, along with the Guidelines International Network and the National Center for Complementary and Integrative Health websites. Eligible guidelines containing recommendations for the treatment and/or management of lung cancer were assessed with the Appraisal of Guidelines, Research and Evaluation II (AGREE II) instrument. RESULTS: From 589 unique search results, 4 guidelines mentioned CIM, of which 3 guidelines made CIM recommendations. Scaled domain percentages from highest to lowest were: scope and purpose (82.4% overall, 76.9% CIM), clarity and presentation (96.3% overall, 63.0% CIM), editorial independence (61.1% overall, 61.1% CIM), rigour of development (62.5% overall, 54.9% CIM), stakeholder involvement (66.7% overall, 42.6% CIM) and applicability (29.9% overall, 18.8% CIM). Quality varied within and across guidelines. CONCLUSION: Guidelines that scored well could serve as a framework for discussion between patients and healthcare professionals regarding use of CIM therapies in the context of lung cancer. Guidelines that scored lower could be improved according to the AGREE II instrument, with insight from other guidelines development resources.
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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 | 0.104 | 0.414 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.024 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".