MétaCan
Menu
Back to cohort
Record W4312924313 · doi:10.33590/emjdermatol/10314478

Serum Sickness-Like Reaction in Children: Review of the Literature

2019· article· en· W4312924313 on OpenAlexaff
Blanca R. Del Pozzo‐Magaña, Alejandro Lazo‐Langner

Bibliographic record

VenueEMJ Dermatology · 2019
Typearticle
Languageen
FieldMedicine
TopicDrug-Induced Adverse Reactions
Canadian institutionsWestern UniversityChildren's Hospital of Western Ontario
Fundersnot available
KeywordsMedicineSerum sicknessErythema multiformeNonsteroidalIntensive care medicineDrug reactionDermatologyToxic epidermal necrolysisAntibioticsArthritisErythemaAdverse drug reactionDrugPediatricsImmunologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Serum sickness-like reaction (SSLR) is an acute inflammatory condition affecting children and adults characterised by the development of erythematous skin lesions and joint swelling with or without fever. Although these features resemble the ones seen in patients with classic serum sickness, the precise pathophysiology of SSLR remains unclear. It is considered that drugs, usually β-lactam antibiotics, and some infectious agents can trigger an immunologic reaction that leads to these clinical manifestations. This condition is usually under-recognised or mistakenly diagnosed as other conditions (e.g., urticaria, urticaria multiforme, reactive arthritis, erythema multiforme) and therefore infrequently reported. Until now, there was no standardised treatment for this condition and controversy regarding the use of antihistamines, nonsteroidal anti-inflammatory drugs, and oral corticosteroids remains. Most of the current literature on SSLR is based on occasional case reports series. The main objective of this manuscript is to offer an organised and updated review of the clinical features and current treatment options for paediatric SSLR, useful for physicians and other health professionals with interest in paediatrics and adverse drug reactions.

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 score0.035
Threshold uncertainty score0.299

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 head0.005
GPT teacher head0.254
Teacher spread0.249 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEMJ DermatologySame topicDrug-Induced Adverse ReactionsFrench-language works237,207