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Record W3133973013 · doi:10.1136/bcr-2020-239434

Bilateral leg swelling as the presenting symptom of Löfgren syndrome in a paediatric patient: a rare presentation of a rare paediatric disease

2021· article· en· W3133973013 on OpenAlexaff
Bailey Komishke, Jessica L. Foulds, Tara McMillan, Nicholas Avdimiretz

Bibliographic record

VenueBMJ Case Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicSarcoidosis and Beryllium Toxicity Research
Canadian institutionsUniversity of AlbertaStollery Children's Hospital
Fundersnot available
KeywordsMedicineErythema nodosumSarcoidosisPresentation (obstetrics)Mediastinal lymphadenopathyRare diseasePopulationSurgeryRadiologyDermatologyDiseasePathologyBiopsy

Abstract

fetched live from OpenAlex

A 17-year-old previously healthy man presented with a 4-week history of progressive bilateral leg swelling with discomfort and erythema, but no signs of arthritis or erythema nodosum. An incidental finding of a query pulmonary nodule on chest X-ray prompted chest CT for further evaluation, revealing bilateral hilar and mediastinal lymphadenopathy. The patient then underwent endobronchial ultrasound and transbronchial needle aspiration biopsies of mediastinal lymph nodes. Biopsies and bronchoalveolar lavage samples were negative for microbiology, including mycobacterial culture. Pathology demonstrated non-caseating granulomas consistent with a diagnosis of sarcoidosis. Weeks later, he developed arthralgias of the left metacarpophalangeal joints and erythema nodosum and was diagnosed with Löfgren syndrome, a phenomenon rarely described in the paediatric population. This case highlights an approach to lower extremity swelling as well as hilar lymphadenopathy in the paediatric population. In addition, it emphasises the importance of multidisciplinary teamwork for accurate and timely diagnoses.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.319
Teacher spread0.294 · 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 designCase report
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

Citations1
Published2021
Admission routes1
Has abstractyes

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