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Record W1995713654 · doi:10.4161/bact.25098

Bacteriophages for managing<i><i>Shigella</i></i>in various clinical and non-clinical settings

2013· article· en· W1995713654 on OpenAlexaff
Lawrence Goodridge

Bibliographic record

VenueBacteriophage · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsShigellosisShigellaBacteriophageDiseasePhage therapyMicrobiologyClinical microbiologyOutbreakVirologyMedicineInfectious disease (medical specialty)Intensive care medicineBiologyBacteriaInternal medicineSalmonellaEscherichia coli

Abstract

fetched live from OpenAlex

The control of shigellosis in humans enjoys a prominent position in the history of bacteriophage therapy. d’Herelle first demonstrated the efficacy of phage therapy by curing 4 patients of shigellosis, and several subsequent studies confirmed the ability of phages to reduce Shigella based infection. Shigella spp continue to cause millions of illnesses and deaths each year and the use of phages to control the disease in humans and the spread of the bacteria within food and water could point the way forward to the effective management of an infectious disease with global influence.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.002

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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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

Citations37
Published2013
Admission routes1
Has abstractyes

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