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Record W7117482279 · doi:10.2196/79579

Jiedu Xiaozhen Granules for Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor–Mediated Skin Toxicity: Protocol for a Randomized Controlled Trial

2025· article· en· W7117482279 on OpenAlexvenueno aff
Shoujiang Hao, Shulan Hao, Xiaoying Zhang, Xiaoyang Qi, Gang Jin, Fangfang Shen, Likun Liu

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Treatments and Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRandomized controlled trialTyrosine kinaseEpidermal growth factorEpidermal growth factor receptorKinaseReceptor tyrosine kinase

Abstract

fetched live from OpenAlex

Background: Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are widely used in the treatment of non-small cell lung cancer due to their precision, efficiency, and ease of use. However, skin rashes induced by EGFR-TKIs are the most common and earliest form of skin toxicity, often affecting the quality of life and treatment compliance of patients and leading to early discontinuation of therapy. These skin reactions may even impact cancer outcomes. In clinical practice, traditional Chinese medicine detoxification granules have shown effectiveness in relieving skin discomforts such as itching, pain, and burning caused by EGFR-TKI therapy. A prior single-arm trial investigating the treatment of targeted drug-induced rashes demonstrated a sustained improvement in rash symptoms with an effectiveness rate of 80.77% and was well tolerated by patients. Objective: As an exploratory clinical study, this randomized controlled trial will preliminarily evaluate the potential efficacy and safety of jiedu xiaozhen (JDXZ) granules in managing EGFR-TKI-related skin toxicities. Methods: This randomized controlled trial will be conducted at Shanxi Provincial Hospital of Traditional Chinese Medicine. A total of 94 patients with confirmed epidermal growth factor receptor gene-mutated non-small cell lung cancer who developed rashes after EGFR-TKI treatment will be enrolled. Patients will be randomly assigned to either a JDXZ traditional Chinese medicine group (group J) or a urea ointment group (group U). The primary outcome will be the severity of the rash as assessed using the National Cancer Institute Common Terminology Criteria for Adverse Events grading. Secondary outcomes will include the WoMo (Wollenberg and Moosmann) score, numerical rating scale, Dermatology Life Quality Index scale, European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 score, median progression-free survival, and changes in the levels of fibroblast growth factor 7 and hepatocyte growth factor in the blood. Adverse reactions will be recorded throughout the study. Data will be analyzed using SPSS. Results: The clinical trial registration was completed in October 2024. This study is currently underway. As of December 1, 2025, a total of 81 eligible participants had been enrolled, all of whom were assigned to groups following the randomization principle. Among them, 42 participants were allocated to the JDXZ group (with an additional 2 participants pending enrollment), and 39 to the control group. Based on the current progress, the estimated trial completion date has been extended to January 31, 2026. Conclusions: The results of this study may help develop an effective treatment for EGFR-TKI-mediated rashes. The findings will be published in academic journals upon the completion of the trial.

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)0.020
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.052
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.015
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0110.005
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0520.006

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 head0.115
GPT teacher head0.504
Teacher spread0.389 · 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 designRandomized trial
Domainnot available
GenreProtocol

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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