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Record W2419070699

A study on mast cell number and lipid profile in oral submucous fibrosis.

2014· article· en· W2419070699 on OpenAlexaff
Benazeer Husain, Balasundari Shreedhar, Mala Kamboj, Srikant Natarajan

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsOral submucous fibrosisMast cellMedicineConnective tissueFibrosisOral mucosaGastroenterologyPathologyLipid profileInternal medicineImmunologyCholesterol
DOInot available

Abstract

fetched live from OpenAlex

AIMS: Prevention of oral precancer is most desirable and should take precedence over its diagnosis and therapy. As both, mast cell counts in connective tissue and serum lipids are altered in Oral Submucous Fibrosis (OSF), so study of both the factors is vitally important in OSF. METHODS: A total of 50 persons were included in the study of which 40 were OSF patients and 10 controls. Tissue sections and blood samples were collected for mast cell count and lipid profile estimation for both OSF patients and controls. RESULTS: Mean mast cells in all grades of OSF were higher as compared to apparently normal appearing oral mucosa but as severity of OSF increases (from Grade II to Grade IV), count of Mast cell decreases. Further, the mean mast cells in OSF Grade II, III and IV groups were found 95.5%, 93.8% and 92.2% higher respectively as compared to normal (highly significant for all the groups p<0.001). The mean serum lipid profile of OSF groups was comparatively lower than age and sex matched healthy controls but was highly significant for all the groups (p<0.001). CONCLUSIONS: Thus, it can be said that in the present study serum lipid profile decreases in OSF patients and mast cell count is increased when compared with apparently normal appearing mucosa but with the advancement of the grades mast cell number decreases in tissue sections of OSF. It can be suggested that biochemical and histological assessment of OSF patients may help in earlier diagnosis and/or prognosis of this disease.

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 score0.017
Threshold uncertainty score0.725

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.031
GPT teacher head0.288
Teacher spread0.257 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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