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Metadata record for the article: Crown-Like Structures in Breast Adipose Tissue of Breast Cancer Patients: Associations with CD68 Expression, Obesity, Metabolic Factors and Prognosis

2021· other· en· W6976918353 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typeother
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerPopulationCohortMalignancyMedical recordMastectomyProportional hazards modelCohort study

Abstract

fetched live from OpenAlex

<b>Summary</b><br> This metadata record provides details of the data supporting the claims of the related article: “Crown-Like Structures in Breast Adipose Tissue of Breast Cancer Patients: Associations with CD68 Expression, Obesity, Metabolic Factors and Prognosis”. The related study examined associations of H&amp;E and CD68-detected crown-like structures of the breast (CLS-B) with clinicopathologic features using chi-squared tests, with metabolic factors using Wilcoxon rank sum tests and with disease free and overall survival using Cox regression models. Type of data: clinical data Subject of data: <i>Homo sapiens; </i>antibodies Sample size: 535 Population characteristics: The population comprised a consecutive cohort of women who underwent treatment for operable breast cancer at Mount Sinai Hospital (Toronto, Canada) between June 1989 and June 1996. Women were included if they had age less than 75 years, complete resection of breast cancer and axillary dissection for previously untreated breast cancer. Women were excluded if they had prior malignancy (except cervical in situ lesion or nonmelanoma skin cancer), a serious coexisting medical condition, including diabetes, use of medications that modified key study variables, or inability to provide consent. Recruitment: Consecutive women were recruited from those women undergoing surgery for breast cancer (see above). Women were excluded if they had prior malignancy (except cervical in situ lesion or nonmelanoma skin cancer), a serious coexisting medical condition, including diabetes, use of medications that modified key study variables, or inability to provide consent. No systematic source of bias was identified. <b>Data access</b> All data underlying the claims of the related article are contained in the tab-delimited text file ‘NRF database.txt’. These data are housed on institutional storage and are not publicly available in order to protect patient privacy, as no Ethics approval to share data was received. However, the data will be made available upon request to individual investigators. Data enquiries should be addressed to the corresponding author. <b>Corresponding author(s) for this study</b> Dr. Martin C. Chang, University of Vermont Medical Center, Department of Pathology &amp; Laboratory Medicine, Main Pavilion 1st Floor, 111 Colchester Avenue, Burlington, VT 05401. Martin.Chang@uvmhealth.org <br> <b>Study approval </b> Ethics oversight was provided by Mount Sinai Hospital, Toronto, Canada, Research Ethics Board. Informed consent was obtained from all participants for both the original prospective trial, and for the use of all data in future research-ethics-board-approved research.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.997

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.256
Teacher spread0.232 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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