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(R) Alopecia areata hair pull test and its correlation to histopathological findings.

2023· article· en· W4389550749 on OpenAlexaff
Sherif Awad, Amal Abdel Rahman, keroles nageh gendy, Michel Ibrahim, Manal Gabril

Bibliographic record

VenueMinia Journal of Medical Research · 2023
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsAlopecia areataDermatologyMedicineTest (biology)CorrelationMathematicsBiology

Abstract

fetched live from OpenAlex

Background: Alopecia areata (AA) is a common non scarring type of hair loss that affects 2% of the general population with unpredictable course. Aim: Histopathological comparing of perifollicular lymphocytic infiltrate in alopecia areata in both longitudinal and transverse sections and its relation to hair pull test. Methods: The study was conducted on 18 AA patients attending the dermatology outpatient clinic, Minia university hospitals from September 2022 to December 2022. Three millimeters punch biopsy was taken from the edge of the lesion in each patient and was processed routinely for longitudinal sections and was stained with hematoxylin and eosin stain then re-embedded and transverse sectioning was performed. Perifollicular mononuclear inflammatory infiltrate was evaluated either significant or not in each case and in each follicle. Result: Significant higher numbers of the terminal hair follicles with significant existence of perifollicular infiltrate were found in transverse sections when compared to longitudinal sections and that immune infiltrates positively correlated to the positive hair pull test.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.121
GPT teacher head0.440
Teacher spread0.319 · 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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Same venueMinia Journal of Medical ResearchSame topicHair Growth and DisordersFrench-language works237,207