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Record W2043142672 · doi:10.1128/jcm.01913-10

Identification by 16S rRNA Gene Sequencing of Negativicoccus succinicivorans Recovered from the Blood of a Patient with Hemochromatosis and Pancreatitis

2011· article· en· W2043142672 on OpenAlexaff
Deirdre L. Church, Keith E. Simmon, Jan Sporina, Tracie Lloyd, Daniel B. Gregson

Bibliographic record

VenueJournal of Clinical Microbiology · 2011
Typearticle
Languageen
FieldMedicine
TopicInfective Endocarditis Diagnosis and Management
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsBacteremiaHemochromatosisBiology16S ribosomal RNACoccus (insect)Ribosomal RNAPancreatitisGeneMicrobiologyClostridiumBlood cultureBacteriaMedicineGeneticsInternal medicineAntibiotics

Abstract

fetched live from OpenAlex

We describe a case of Negativicoccus succinicivorans bacteremia in an adult man with hemochromatosis and acute pancreatitis. Conventional phenotypic tests and commercial identification systems failed to definitively identify the tiny anaerobic Gram-negative coccus isolated from two sets of blood cultures. The bacterium was identified by 16S rRNA gene sequencing and analysis using the SmartGene Integrated Database Network System software. This is the first published report of the recovery of this organism from a patient with invasive infection.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.284
Teacher spread0.255 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

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