The efficacy and safety of Kyung-Ok-Ko on cancer-related fatigue in lung cancer patients
Bibliographic record
Abstract
BACKGROUND: Cancer-related fatigue (CRF) is a major symptom experienced by lung cancer patients receiving chemotherapy and radiation therapy. Since CRF has a multidimensional influence on cancer patients, they may experience physical weakening, a decline in cognitive function, and depression from emotional consequences. Kyung-Ok-Ko is used for improving fatigue or weak physical constitution. It is known to be effective in immune activation, reducing fatigue, and enhancing cognitive function. Although Kyung-Ok-Ko is clinically used for the treatment of CRF, its efficacy and safety against CRF in lung cancer patients are yet to be studied. Therefore, we aimed to investigate the efficacy and safety of Kyung-Ok-Ko. METHODS: This is a randomized, placebo-controlled, patients-assessor blind, parallel-group, single-center clinical trial. Lung cancer patients with CRF, after termination of chemo or radiation therapies, are randomized in a 1:1 ratio to receive either Kyung-Ok-Ko or placebo for 6 weeks. The primary outcome is Brief Fatigue Inventory (BFI). The secondary outcomes include Visual Analog Fatigue Scale (VAFS), Functional Assessment of Cancer Therapy (FACIT) Fatigue scale, Hospital Anxiety Depression Scale (HADS), Montreal Cognitive Assessment Korean version (MoCA-K), and Korean pattern identification questionnaire. Adverse events are evaluated by Common Terminology Criteria for Adverse Events (CTCAE). All outcomes and adverse events are assessed at the baseline, mid-treatment, post-treatment, and at 1-month follow-up. DISCUSSION: This study investigates whether Kyung-Ok-Ko can alleviate CRF in lung cancer patients. The results of this study will provide clinical evidence for the application of Kyung-Ok-Ko in the treatment of CRF in lung cancer patients. TRIAL REGISTRATION: Korean Clinical Trial Registry (http://cris.nih.go.kr; registration number: KCT000666).Trial status: Currently, participant recruitment is ongoing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".