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Record W1864261161 · doi:10.1111/dth.12225

Livedoid vasculopathy and high levels of lipoprotein (a): response to danazol

2015· article· en· W1864261161 on OpenAlexaff
Paulo Ricardo Criado, Danielle Priscilia de Souza EspinelI, Neusayuriko Sakai Valentef, Afsáneh Alavi, Robert S. Kirsner

Bibliographic record

VenueDermatologic Therapy · 2015
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDanazolInternal medicineGastroenterologyLipoprotein(a)LipoproteinDermatologySurgeryCholesterolEndometriosis

Abstract

fetched live from OpenAlex

Livedoid vasculopathy (LV) is a thrombo occlusive disorder presenting with recurrent painful ulcers of lower extremities. Association of LV with increased level of lipoprotein (a) (LP(a)), a risk factor for cardiovascular disease, has been reported. Danazol has been used with success in the management of LV, but none of the previous studies looked at the correlation between response to the treatment and level of LP(a). The aim of this study was to demonstrate the efficacy of low-dose danazol in the treatment of LV and its effects on LP(a). We present four cases with LV who were successfully treated with low-dose danazol, assessing the clinical characteristics and laboratory tests including the level of LP(a). The average age of the patients was 45 years and the mean duration of the disease was 19 years. The treatment regime of danazol 200 mg daily led to complete healing of ulcers and reduction in pain and a 70% (ranging from 52 to 87%) reduction in the level of LP(a). The limitation of this study is "small sample size." In our patients with LV, low-dose danazol led to clinical improvement along with significant reduction in the level of LP(a).

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)0.002
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.293
Teacher spread0.243 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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