MétaCan
Menu
Back to cohort
Record W2341664651 · doi:10.22374/cjgim.v8i1.97

The Pathognomonicity of Gottron’s Sign

2013· article· en· W2341664651 on OpenAlexafffundvenue
Heather M. Babcock BSc, Mohammed Osman, Tiffany Kwok, Stephen Chihrin, Stephanie O. Keeling MD MSc, Sumit R. Majumdar

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesFondation pour la Recherche MédicaleUniversity of Alberta
KeywordsMedicineDermatomyositisDermatologyPathognomonicMalignancyBiopsyPathology

Abstract

fetched live from OpenAlex

Summary This article presents the case of a previously healthy 43-year-old female who presented with a 3-month history of progressive, symmetrical, bilateral, proximal muscle weakness accompanied by a violaceous-to-erythematous rash involving her hands, arms, thighs, chest, and face. She had conspicuous non-edematous periorbital violaceous patches with telangiectasia and prominent warm violaceous macules overlying the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints. Muscle biopsy confirmed dermatomyositis. Gottron’s sign is the most specific cutaneous finding of dermatomyositis and is present in at least 70% of patients. The lesions begin as non-palpable flat macules or patches (Gottron’s “sign”) or are firm and raised (Gottron’s “papules”), but the lesions eventually coalesce into raised non-blanching plaques that occur over bony prominences – typically the MCP, PIP, and/or distal interphalangeal joints. Gottron’s sign (and papules) are pathognomonic for dermatomyositis, although some other conditions may have similar presentations. Gottron’s sign must always be explained, as dermatomyositis may be primary or secondary to malignancy or other connective tissue diseases, and none of the conditions that make up the differential diagnosis are benign.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.988

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)0.0010.000

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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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 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

Citations2
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of General Internal MedicineSame topicInflammatory Myopathies and DermatomyositisFrench-language works237,207