Sixteen-year history with high dose intravenous vitamin C treatment for various types of cancer and other diseases
Bibliographic record
Abstract
The authors (HDR, JAJ) have previously reported on the use of high dose intravenous vitamin C in the treatment of patients with various types of cancer. Research conducted at The Center has also been published to help explain the scientific basis for the dynamics of intra venous vitamin C. Many health care workers are wary of giving high dose vitamin C to patients due to the warning that “one could develop kidney stones with high dose vitamin C.” The possibility of kidney stones does exist, theoretically, because vitamin C (ascorbic acid) is water soluble and is excreted by the kidneys as oxalic acid. Since most kidney stones consist of some form of oxalate, it would seem to follow, to some people, there must be kidney stones. In reality, this never happens. Another question comes to mind, why are there thousands of people with kidney stones who DO NOT take large doses of vitamin C? Humans must get their vitamin C from the diet or as supplements. Millions of years ago humans lost the enzyme Lgulono-g-lactone oxidase, a key in the conversion of glucose to vitamin C. If the above theory of vitamin C causing kidney stones is correct, why is it that animals are not suffering an epidemic of kidney stones? Based on body weight, the smallest to the biggest animal can manufacture a daily amount of vitamin C that can vary from 1 gram to over 20 g (about 12.5 to 250 times the RDA for humans)! In addi-
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".