Systematic Review of Guidelines for the Management of Patients with Cancer Pain
Bibliographic record
Abstract
Background To improve the quality of pain management in cancer patients, clinical guidelines have been published and revised around the world. Whereas, there are large gaps in evidence-based resources across cancer pain management guidelines, and there is an urgent need for high-quality systematic evaluation of guidelines to bridge the practice gap. Objective To systematically review the relevant guidelines for treating cancer pain patients, analyze the similarities and differences between the recommendations of each guide, and provide an evidence-based decision reference for clinical practice. Methods A systematic search of PubMed, Web of Science, Cochrane Library, CNKI, Wanfang Data, CBM, BP, SIGN, NGC, Medical Communication, and the websites of relevant societies and industry bodies for guidelines on the management of patients with cancer pain was performed. The search timeframe was from the construction of the database to October 9, 2023. The literature was screened by two investigators according to the inclusion and exclusion criteria, and the guidelines were evaluated for quality using the Appraisal of Guidelines for Research & EvaluationⅡ (AGREE Ⅱ) to summarise the recommendations of each guideline related to managing pain in cancer patients. Results Seven guidelines were ultimately included, developed for the period 2016-2023, including two from the United States, two from Switzerland, one from Canada, one from Latin America, and one from Japan, all with guideline recommendation level A. The related recommendations were mainly focused on 4 aspects: cancer pain assessment, pharmacological management, non-pharmacological management, and cancer pain education. Conclusion The recommendations of the 7 guidelines can provide a new reference for the practice of cancer pain management in China. It is suggested that clinical practitioners should consider the specific conditions of patients and work with multidisciplinary cooperation to truly achieve the "5A" goal of cancer pain management.
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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 | 0.030 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".