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Record W3002788820 · doi:10.1111/jocd.13290

Correlation between serum IL 37 levels with keloid severity

2020· article· en· W3002788820 on OpenAlexaboutno aff
Fathia M. Khattab, Mai Samir

Bibliographic record

VenueJournal of Cosmetic Dermatology · 2020
Typearticle
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsnot available
Fundersnot available
KeywordsKeloidMedicineExtracellular matrixCorrelationInternal medicineDermatologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Keloids are dermal fibroproliferative disorders that characterized by over deposition of components of the extracellular matrix. Interleukin 37 (IL-37) is known by its ability to inhibit the proliferation of keloid fibroblasts by inhibiting extracellular matrix production induced by transforming growth factor β (TGF-β). Thus, Il-37 is suggested to be used as an early preventive treatment for keloids. AIMS: This study aimed to evaluate the correlation between serum levels of IL37 level and the keloid severity. PATIENTS/METHODS: This is a cross-sectional analytic study involving thirty-two patients diagnosed clinically as having Keloid. An assessment of keloid severity was conducted by using Vancouver Scar Scale (VSS). Blood samples were collected from every patient to measure and assess the serum levels of IL37. RESULTS: A negative correlation was found between IL37 level and the keloid severity (P = .0001; r = -.737). Also, there was a nonsignificant correlation between IL37 levels in patient with keloid and age, gender, duration of lesions, and family history. CONCLUSION: Lower level of plasma IL 37 could be an indicator of the severity of Keloids.

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 score0.064
Threshold uncertainty score0.395

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 integrity0.0000.001
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 head0.051
GPT teacher head0.318
Teacher spread0.267 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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