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Record W2124374772 · doi:10.1517/13543770802557740

Non-antibiotic strategies for the prevention/treatment of<i>Clostridium difficile</i>infection

2008· article· en· W2124374772 on OpenAlexaff
Rhonda KuoLee, Wangxue Chen

Bibliographic record

VenueExpert Opinion on Therapeutic Patents · 2008
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsNational Research Council CanadaInstitute for Biological Sciences
Fundersnot available
KeywordsClostridium difficileMetronidazoleMedicineAntibioticsVancomycinIntensive care medicineEnterocolitisDiarrheaDiseaseInternal medicineMicrobiologyBacteriaBiologyStaphylococcus aureus

Abstract

fetched live from OpenAlex

Background: Clostridium difficile infection has become a serious concern in both hospital and secondary healthcare environments. In the presence of repeated or prolonged antibiotic treatment, the C. difficile spores can germinate in the colon and produce toxins that cause colonic inflammation and diarrhea. The standard treatment for C. difficile-associated disease (CDAD) usually involves the withdrawal of the antibiotic treatment that led to the CDAD followed by a course of oral metronidazole or vancomycin, but there has been an increasing number of treatment failures and recurrences of disease. Over the past 10 – 15 years, researchers have begun exploring the possibility of using alternative means to combat C. difficile infection. Objective/methods: Over the course of the past 5 years, there has been a considerable amount of patent literature focused on non-antibiotic alternatives, including passive and active immunizations, monoclonal antibodies, antitoxins, inert binders and probiotic therapies. Results/conclusion: Current antibiotic therapies for the treatment of CDAD are not as effective as they once were. There is some promising work on non-antibiotic alternatives for CDAD prevention and treatment.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.835

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 head0.130
GPT teacher head0.376
Teacher spread0.247 · 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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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