MétaCan
Menu
Back to cohort
Record W2747051354 · doi:10.14740/wjon1054w

Role of Taxanes in Triple-Negative Breast Cancer: A Study From Tertiary Cancer Center in South India

2017· article· en· W2747051354 on OpenAlexvenueno aff
KC Lakshmaiah, Govind Babu, Lokanatha Dasappa, Linu Abraham Jacob, Suresh Babu, K.N. Lokesh, A.H. Rudresha, L K Rajeev, Smitha Saldanha, G.V. Giri, Deepak Koppaka

Bibliographic record

VenueWorld Journal of Oncology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriple-negative breast cancerAnthracyclineTaxaneBreast cancerOncologyInternal medicineRegimenCancerSingle CenterChemotherapy

Abstract

fetched live from OpenAlex

BACKGROUND: Breast cancer is the most common female cancer seen globally. Triple-negative breast cancer (TNBC) is a special subtype without any obvious target and optimum treatment remains challenging. The aim was to study the clinical, pathological profile and treatment outcome of TNBC patients. METHODS: This was a retrospective observational study of TNBC patients diagnosed from January 2010 to June 2012 at a tertiary cancer center in South India. Patient's clinical and pathological characteristics were studied. The 5-year estimate of survival for non-metastatic TNBC was done using the Kaplan-Meier method. RESULTS: Out of 804 patients of breast cancer, 237 were diagnosed as TNBC. The median age was 45 years and 58% were premenopausal. The 5-year disease-free survival (DFS) and overall survival (OS) for non-metastatic TNBC patients were 59% and 74%, respectively. The addition of a taxane to anthracycline-based regimen did not show a significant difference in DFS (P = 0.885) as well as OS (P = 0.856). CONCLUSION: The role of adding taxanes to anthracycline-based chemotherapy in adjuvant setting for TNBC remains controversial and larger prospective studies are warranted.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.309
Teacher spread0.297 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of OncologySame topicBreast Cancer Treatment StudiesFrench-language works237,207