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Record W4417212805 · doi:10.1016/j.jtcme.2025.12.002

Oral Chinese herbal medicine in combination with opioids for treatment of cancer pain: A systematic review and meta-analysis

2025· review· en· W4417212805 on OpenAlexaff
Thuy Le, Nguyen Bao Ngoc, Nhuan P. Nghiem, Yuling Zheng

Bibliographic record

VenueJournal of Traditional and Complementary Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAdverse effectCancerAlternative medicineTraditional Chinese medicineOpioidCancer pain

Abstract

fetched live from OpenAlex

Background and aim: While oral Chinese herbal medicine (OCHM) is frequently used for cancer pain (CP), its combined effects with opioids remain unclear. This study aims to evaluate the efficacy and safety of OCHM combined with opioids in patients with moderate to severe CP. Experimental procedure: We systematically searched five Chinese and English databases up to December 30th, 2024, for randomized controlled trials comparing OCHM plus opioids versus opioids alone. Primary outcomes were pain relief and pain intensity. Secondary outcomes included onset and duration of pain relief, Karnofsky Performance Status (KPS) score, and adverse events. Results: < 0.001). Conclusion: OCHM plus opioids improved pain relief, enhanced the quality of life, reduced opioid-related adverse events in patients with moderate to severe CP compared to opioids alone.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.018
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.136
GPT teacher head0.396
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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