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Record W2792975772 · doi:10.21037/jlpm.2018.02.05

Laboratory diagnosis of Clostridium difficile infection

2018· article· en· W2792975772 on OpenAlexfundno aff
Katherine Kendrick

Bibliographic record

VenueJournal of Laboratory and Precision Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsnot available
FundersConsortium canadien en neurodégénérescence associée au vieillissementAmerican College of Gastroenterology
KeywordsClostridium difficilePseudomembranous colitisMicrobiologyMedicineAntibiotic-associated diarrheaDiarrheaAsymptomatic carrierAntibioticsToxic megacolonOutbreakAnaerobic bacteriaClostridium difficile toxin AClostridiumVirologyDiseaseInternal medicineBiologyBacteria

Abstract

fetched live from OpenAlex

Clostridium difficile infection (CDI) is a serious and sometimes life-threatening illness caused by toxin release from Clostridium difficile (CD), a gram-positive anaerobic bacterium. Infection with CD can cause clinical manifestations in a spectrum from asymptomatic carrier states to pseudomembranous colitis and toxic megacolon. Accurate diagnosis of CDI depends on early recognition of clinical symptoms of diarrhea, fever, and cramps especially after antibiotic use. Bacterial culture can be performed for epidemiological and antibiogram purposes during outbreaks of CDI. Culture, enzyme immunoassays (EIA), and molecular assays are useful for diagnosis of CDI. Toxigenic culture is useful to determine the cytopathic effect of the bacteria. Current Infectious Disease Society of America (IDSA) and American College of Gastroenterology (ACG) guidelines recommend using nucleic acid amplification tests or glutamate dehydrogenase (GDH) antigen followed by EIA testing for CD toxin A and B. Future studies for CDI diagnosis are looking toward toxin identification and the use of metabolomic analysis.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.330
Teacher spread0.304 · 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.

Study designBench or experimental
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

Citations3
Published2018
Admission routes1
Has abstractyes

Explore more

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