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Record W2103546345 · doi:10.1186/1479-5876-9-25

Intravenous ascorbic acid to prevent and treat cancer-associated sepsis?

2011· article· en· W2103546345 on OpenAlexaff
Thomas E. Ichim, Boris Minev, Todd A. Braciak, Brandon Luna, Ron Hunninghake, Nina Mikirova, Jackson James, Michael J. González, Jorge R. Miranda-Massari, Doru T Alexandrescu, Constantin A Dasanu, Vladimir Bogin, Jānis Ancāns, R. Brian Stevens, Boris Markosian, James Koropatnick, Chien-Shing Chen, Neil H Riordan

Bibliographic record

VenueJournal of Translational Medicine · 2011
Typearticle
Languageen
FieldNursing
TopicVitamin C and Antioxidants Research
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsAscorbic acidSepsisMedicineCancerInflammationAdjuvantSystemic inflammatory response syndromeInternal medicinePharmacologyBioinformaticsIntensive care medicineImmunologyChemistryBiology

Abstract

fetched live from OpenAlex

The history of ascorbic acid (AA) and cancer has been marked with controversy. Clinical studies evaluating AA in cancer outcome continue to the present day. However, the wealth of data suggesting that AA may be highly beneficial in addressing cancer-associated inflammation, particularly progression to systemic inflammatory response syndrome (SIRS) and multi organ failure (MOF), has been largely overlooked. Patients with advanced cancer are generally deficient in AA. Once these patients develop septic symptoms, a further decrease in ascorbic acid levels occurs. Given the known role of ascorbate in: a) maintaining endothelial and suppression of inflammatory markers; b) protection from sepsis in animal models; and c) direct antineoplastic effects, we propose the use of ascorbate as an adjuvant to existing modalities in the treatment and prevention of cancer-associated sepsis.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.554

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)0.0010.000

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 head0.051
GPT teacher head0.336
Teacher spread0.285 · 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 designObservational
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

Citations27
Published2011
Admission routes1
Has abstractyes

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