Impact of Intravenous Vitamin C Administration in Reducing Severity of Symptoms in Breast Cancer Patients During Treatment
Bibliographic record
Abstract
Introduction Alternative medicine during treatment is often used to make the quality of life (QoL) better. Women with early-stage breast cancer, particularly the ones who possess lower QoL, are more prone to opt for complementary medicine. This study aims to explore the effects exerted by intravenous vitamin C (IVC) on symptoms and adverse events associated with breast cancer treatment. Methods This single-center, parallel-group, single-blind interventional study was conducted in the oncology ward of a tertiary care hospital in Pakistan. For this study, after informed consent was taken, breast cancer patients with Union for International Cancer Control stages IIA to IIIb were included in the study. Three hundred and fifty (n = 350) patients were randomized into two groups at a ratio of 1:1. Study group was randomized to receive 25 grams per week of IVC at a rate of 15 grams per hour for four weeks in addition to their current standard treatment, and the control group received placebo (normal saline drip with label removed) in addition to their current standard treatment. Results In patients who had received IVC, there was a significant decrease in the mean severity score after 28 days for the following symptoms: nausea (2.65 ± 0.62 vs. 2.59 ± 0.68; p-value: 0.0003), loss of appetite (2.26 ± 0.51 vs. 2.11 ± 0.52; p-value: 0.007), tumor pain (2.22 ± 0.45 vs. 1.99 ± 0.40, p-value: <0.0001), fatigue (3.11 ± 0.32 vs. 2.87 ± 0.29; p-value: <0.0001), and insomnia (2.59 ± 0.35 vs. 2.32 ± 0.36, p-value: <0.0001). Conclusion Our study showed improvement in the mean severity score of nausea, fatigue, tumor pain, loss of appetite, and fatigue. More studies are also needed to assess the long-term effects of IVC in the cancer management. This shall help incorporate the use of IVC in standard practice to make the journey of cancer management comfortable for the patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".