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Record W2022036643 · doi:10.2302/kjm.49.80

Seeing through the Stratum Corneum

2000· article· en· W2022036643 on OpenAlexaff
R. Marks

Bibliographic record

VenueThe Keio Journal of Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsSKiN Health
Fundersnot available
KeywordsStratum corneumStainingVital stainBiopsyMelaninPathologySkin biopsyChemistryDermatologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

The stratum corneum (SC) provides a vital barrier membrane between the external environment and the vulnerable internal tissues of the skin. It impedes the flow of water, the penetration of xenobiotics, and invasion of pathogenic micro-organisms. It also has protective capacity against ultraviolet radiation and thermal injury. As routine histopathology provides a misleading picture of a disorganized and shadowy SC, we would recommend the skin surface biopsy technique. This painless technique is easy and reliable in obtaining information from the SC. It demonstrates the geometric patterns of the surface, the openings of the eccrine ducts and hair follicles. The skin surface biopsy technique is also ideal for the investigation of the in situ microbiology of skin. Staining with periodic acid Schiff reagent makes it possible to see ringworm fungi, pityriasis versicolor, candida species, or erythrasma micro-organisms. Scanning electron microscopy can be employed when the higher magnification is needed. Histochemical applications include silver staining for melanin particle, potassium ferricyanide staining for blood pigments and lipid staining with Sudan red, for sebum. The rate of movement of topically applied drugs into the skin can be measured using the skin surface biopsy technique. The concentration of radiolabelled drugs can be counted and compared. Comedogenicity and DNA analysis are other applications of this non-invasive technique.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.296
Teacher spread0.270 · 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.

Study designNot applicable
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

Citations4
Published2000
Admission routes1
Has abstractyes

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