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Record W4391723580 · doi:10.12775/jehs.2023.58.005

Paraneoplastic pemphigus and reported, identified underlying diseases

2024· article· en· W4391723580 on OpenAlexaff
Paweł Iwańczuk

Bibliographic record

VenueJournal of Education Health and Sport · 2024
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Bullous Skin Diseases
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPemphigusParaneoplastic pemphigusDermatologyMedicineImmunologyAutoantibodyAntibody

Abstract

fetched live from OpenAlex

Introduction: Pemphigus is a rare group of autoimmune diseases that promote development of various skin eruptions, the most commonly it presents with blisters. The several types of pemphigus are: pemphigus vulgaris, pemphigus foliaceus, intraepidermal neutrophillic IgA dermatosis and especially rare type- paraneoplastic pemphigus, which is main topic of this publication. Paraneoplastic pemphigus is a disease which affects patients with developing carcinogenesis process. Usually it is a malignant process, but there are also some cases which describe paraneoplastic pemphigus associated with benign tumors. Because of its occurence our current level of knowledge about different aspects of paraneoplastic pemphigus is not final and as comprehensive as researchers and patients wish. Aim of study: The main purpose of this publication is presentation a review of literature about paraneoplastic pemphigus- its epidemiology, physiopathology, pathomorfology, clinical features, and description of different types of benign or malignant processes which concomitence with paraneoplastic pemphigus. State of knowledge: Paraneoplastic pemphigus is a rare autoimmune disease, which aspects are not widely described, but there are some reports about epidemiology, pathophysiology, histopathology, treatment and associated confirmed malignancies. The challenge for future is to formulate clear, formal, treatment protocols for paraneoplastic pemphigus. Conclusion: Paraneoplastic pemphigus is a very fascinating disease for researchers because there are still very limited reports and statistics about this very rare disorder. Nonetheless year by year our level of knowledge about pathogenesis, clinical features, associated malignant and benign processes is growing and it can affect more personalized, effective treatment methods and protocoles.

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 score0.137
Threshold uncertainty score0.722

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)0.0000.000

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 head0.037
GPT teacher head0.380
Teacher spread0.343 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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