Best evidences summary on acupressure relieved chemotherapy induced nausea and vomiting in patients with cancer
Bibliographic record
Abstract
ObjectiveTo retrieve，evaluate and summarize best evidences on acupressure relieved chemotherapy induced nausea and vomiting in patients with cancer.MethodsIt retried British Medical Journal Best Practice，Up To Date，Guidelines International Network，National Health and Healthcare，JBI COnNECT+，Cochrane evidence-based Medicine Database，Canada Registered Nurses' Association of Ontario，Mosby's Nursing Consult，Elsevier，OVID，PubMed，CNKI，Wanfang Data，VIP，CBM.All evidence on acupressure relieved chemotherapy induced nausea and vomiting in patients with cancer，including guidelines，evidence summaries and systematic evaluations，were collected.The quality of included literatures were evaluated.Then，evidences were extracted from literatures that met the quality standards.ResultsA total of 6 papers were included in this study，including 3 guidelines，3 systematic evaluations.3 guideline evidences showed that acupressure reduced nausea，retching and vomiting，but the effect was very small.It recommended taking this measure for individual patients according personal preference and professional judgment.The results of 3 systematic reviews showed that acupressure could eliminate symptoms of acute and delayed nausea.But it cannot be proven that acupressure could improve vomiting.ConclusionsPresent evidences showed that acupressure could partly relieve CINV in patients with cancer，but it needs more high⁃quality，large⁃sample clinical trials，more systematic reviews and evidence summary to prove the actual effect.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".