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[Clinical observation on acupuncture for symptom burden in gastric cancer patients undergoing adjuvant chemotherapy after gastrectomy].

2022· article· en· W4309515000 on OpenAlexaboutno aff
Xuesong Chang, Yanjuan Zhu, Jianjun Peng, Xiaoyu Wu, Wei Wang, Yi‐Hong Liu, Dan-Dan Zhan, Yadong Chen

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicAcupuncture Treatment Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineZusanliAcupunctureChemotherapySurgeryElectroacupunctureAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To observe the efficacy of acupuncture on symptom burden in patients with gastric cancer during adjuvant chemotherapy after gastrectomy. METHODS: points selected by the meridian heat sensing technique. Electroacupuncture was connected to ipsilateral Zusanli (ST 36) and Neiguan (PC 6), with continuous wave, 2 Hz in frequency for 20 min. The Edmonton symptom assessment system (ESAS) score was observed on day 1-7, 14, and 21 of each cycle of chemotherapy respectively in the 3 groups. RESULTS: <0.05). CONCLUSION: Acupuncture shows promising effect in controlling symptom burden during adjuvant chemotherapy in gastric cancer patients after gastrectomy.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.324
Teacher spread0.283 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2022
Admission routes1
Has abstractyes

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