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Record W70385687 · doi:10.1155/2008/246468

A Case of Necrotizing Fasciitis due to <i>Streptococcus pneumoniae</i> Serotype 5 in Saskatchewan

2007· article· en· W70385687 on OpenAlexaffabout
Meenakshi Dawar, Bob Russell, Karen McClean, Paul N. Levett, Gregory J. Tyrrell, James Irvine

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2007
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsSaskatchewan HealthSaskatchewan Disease Control LaboratoryProvincial Laboratory of Public HealthPublic Health Agency of CanadaRoyal University HospitalSaskatchewan Health Authority
Fundersnot available
KeywordsStreptococcus pneumoniaeFasciitisMedicineSerotypePneumococcal vaccineEtiologyPneumoniaImmunologyPneumococcal pneumoniaImmunosuppressionOutbreakDermatologyMicrobiologyInternal medicineAntibioticsSurgeryVirologyBiology

Abstract

fetched live from OpenAlex

Necrotizing fasciitis due to Streptococcus pneumoniae is a rare and grave condition, and only a few cases have been reported. Suggested risk factors include minor trauma, systemic lupus erythematosus, immunosuppression secondary to medication, use of intramuscular anti-inflammatories and alcoholism. A fatal case of pneumococcal necrotizing fasciitis that occurred in a 51-year-old woman with a history of alcohol abuse and oral anti-inflammatory use is presented. Her condition was caused by a multi-etiology outbreak of community-acquired pneumonia, from which S pneumoniae serotype 5 was also isolated. The case description outlines the subtle presentation and rapid clinical progression of this condition. Because serotype 5 antigen is included in the polysaccharide 23-valent pneumococcal vaccine, the present case highlights the importance of pneumococcal immunization programs in Canada.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.735

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.258
Teacher spread0.251 · 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

Citations12
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Infectious Diseases and Medical MicrobiologySame topicStreptococcal Infections and TreatmentsFrench-language works237,207