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Chronic Inflammatory Mediators Induced Malignant Changes in Tumor Microenvironment of Oral Squamous Cell Carcinoma-New Insight

2018· article· en· W3202554305 on OpenAlexvenueno aff
TG Shrihari

Bibliographic record

VenueJournal of Analytical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
Fundersnot available
KeywordsTumor microenvironmentCancer researchImmune systemMedicineCancerInflammationImmunologyCarcinogenesisTumor progressionCarcinogenInnate immune systemBiologyInternal medicine

Abstract

fetched live from OpenAlex

Oral cancer is a major cause of mortality and morbidity across the world. Because of extensive use of carcinogenic products such as Tobacco (Smoking or Chewable form), alcohol consumption and some cases due to infectious agents such as HPV (Human papilloma virus) induced oro-pharyngeal carcinoma. These carcinogens induce inflammatory changes in the inflammatory microenvironment of oral cavity. Oral Squamous cell carcinoma is the most common cancer of oral cavity. Chronic inflammatory mediators in tumor microenvironment are adaptive and innate immune cells such as macrophages, neutrophils, T- lymphocytes,mast cells,B-lymphocytes and their secreting factors such as proteases, ROS and cytokines, which in turn activates transcriptional factors (NF-KB,STAT-3) secreted by these immune cells and tumor cells bring about malignant changes. This article briefs about chronic inflammatory mediators in tumor microenvironment of oral Squamous cell carcinoma and their role in tumor progression.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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
Published2018
Admission routes1
Has abstractyes

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