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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.713
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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