The guidelines of pain management in palliative care:a systematic evaluation
Bibliographic record
Abstract
ObjectiveTo systematically evaluate the relevant guidelines for pain management in hospice care and compare the similarities and differences among the recommendations，so as to provide references for clinical evidence-based decision making.MethodsThe relevant guidelines were searched in CBM，Wanfang Data，CNKI，PubMed，NGC，GIN and the websites of relevant societies and industry institutions.The retrieval period was from the database establishment to March 29，2020.Two researchers screened literatures according to the include and exclude standard.The quality of guidelines were evaluated by using AGREEⅡ，and the recommendations for pain management in palliative care were summarized.ResultsIn the end，eight guidelines were included，and the formulation time was from 2011 to 2019.Among which，three guidelines were from Canada，two were from the United States，one was from the United Kingdom，one was from Europe and one was from China.The recommendation level of seven guidelines was A，and one was B.Related recommendations mainly involved pain assessment，drug management，psychosocial support，and pain education.ConclusionThe recommendations of eight guidelines could provide a basis for further regulating the pain management of terminal patients.It was suggested that，based on referring to relevant foreign guidelines，clinical staff should combine the specificity of China's regional culture and value needs to establish practical guidelines for pain management in palliative care.
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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 | 0.060 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".