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Record W2525282501

Sixteen-year history with high dose intravenous vitamin C treatment for various types of cancer and other diseases

2002· article· en· W2525282501 on OpenAlexvenueno aff
Jackson James, Hugh D. Riordan, Nancy L. Bramhall, Sharon Neathery

Bibliographic record

VenueJournal of orthomolecular medicine · 2002
Typearticle
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsnot available
Fundersnot available
KeywordsKidney stonesAscorbic acidVitaminVitamin CKidneyMedicineScurvyCancerPhysiologyInternal medicineChemistryFood science
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2002
Admission routes1
Has abstractyes

Explore more

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