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Record W1970466856 · doi:10.1159/000226456

Plasma Levels of Retinol in Cancer Patients Supplemented with Retinol

2009· article· en· W1970466856 on OpenAlexaff
André Lacroix, Pangala V. Bhat, Athanasios Karabatsos, Patrick Couture, Jean Latreille, R Beaulieu, Michael Bourque

Bibliographic record

VenueOncology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsUniversité de MontréalMontreal Clinical Research InstituteHôtel-Dieu de Montréal
Fundersnot available
KeywordsRetinolMedicineInternal medicineDiscontinuationEndocrinologyCancerChemotherapyVitaminGastroenterology

Abstract

fetched live from OpenAlex

Previous studies have indicated that plasma levels of retinol are decreased in some cancer patients and that lower levels of retinol could be associated with a poor response to chemotherapy. This pilot study was conducted to determine whether it is possible to increase plasma levels of retinol in cancer patients by supplementation with retinol. Plasma levels of retinol were measured by high-performance liquid chromatography in 46 patients treated with chemotherapy for various malignancies and in 43 control individuals; cancer patients were supplemented orally either with 25,000 or 50,000 IU of retinol daily during up to 3 months. Initial levels of retinol were lower in cancer patients than in the control group; the decrease was significant in women with liver metastases but not in men. Women supplemented with 25,000 IU had a significant increase of their retinol levels after 1 month but this effect was not maintained during continued supplementation; in women receiving 50,000 IU daily, a sustained increase in retinol level was maintained during the 3 months of supplementation. In men, a similar trend was produced by the supplementation but the increases were not significant. Retinol levels decreased to initial levels within 1 month of discontinuation of supplementation, indicating the need for continuous supplementation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.453

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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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 designBench or experimental
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

Citations4
Published2009
Admission routes1
Has abstractyes

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