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Record W4225083936 · doi:10.5281/zenodo.6502368

Prognostic Significance of Systemic Cholesterol Profile in Patients with Breast Cancer

2022· article· en· W4225083936 on OpenAlexaboutno aff
Radhika Chowdary

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineBreast cancerCancerOncologyCholesterol

Abstract

fetched live from OpenAlex

Breast cancer ranks as the number one cancer among Indian females with survival as low as 66.1%. Relationship between cholesterol and breast cancer has triggered special interest due to their role in important cellular processes that steer toward carcinogenesis. The interplay between cholesterol and tumor development have been studied in experimental breast cancer models. However, epidemiological data reveal conflicting results, that need to be integrated and put into appropriate viewpoint. This study aims to investigate and corroborate the impact of total cholesterol (TC), triglyceride (TG), VLDL, LDL and HDL cholesterol on the disease-free and overall survival of patients with breast cancer. This study retrospectively analyzed 50 breast cancer patients who underwent radical surgery and attended follow-up visits at KIMS Hospitals. The blood lipid levels such as TC, TG, VLDL, LDL and HDL cholesterol were collected and analyzed from the database of Department of Laboratory Medicine. Potential prognostic factors including age, menopause, grade, receptor status, systemic cholesterol profile etc., were analyzed by univariate and multivariate analysis. TC less than 180mg/dL was associated with disease relapse in univariate analysis. TG, VLDL, LDL and HDL cholesterol were not significantly correlated either to disease-free or overall survival. 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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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.210
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

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