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Record W4392827543 · doi:10.53555/sfs.v10i6.2257

Study Of Some Immunological Parameters In Breast Cancer Patients

2023· article· en· W4392827543 on OpenAlexvenueno aff
Khalid Rashed alzaailiay, Yasir Abdullah bin-khalifah, Rasha Awwadh Eid Alshalawi, Meshael Oudah Dawi Alotaibi, Mofareh mohammed alshahrani, Abdullah M. AlShahrani

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerMedicineOncologyCancerInternal medicine

Abstract

fetched live from OpenAlex

Breast cancer is one of the most prevalent forms of cancer affecting women worldwide. Understanding the immunological parameters in breast cancer patients is crucial for developing targeted therapies and improving patient outcomes. This study aimed to investigate various immunological parameters in breast cancer patients at the Master level. The methods involved collecting blood samples from patients and analyzing different immune markers. Results showed significant alterations in immune cell populations, cytokine levels, and immune cell function in breast cancer patients compared to healthy controls. The implications of these findings are discussed in detail, highlighting the potential for immunotherapy in breast cancer treatment. In conclusion, this study sheds light on the importance of analyzing immunological parameters in breast cancer patients to guide personalized treatment strategies.

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.125
GPT teacher head0.300
Teacher spread0.175 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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