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Record W1987719209 · doi:10.1086/504429

Treatment of Shiga-Like Toxin--Producing Escherichia coli Infection

2006· letter· en· W1987719209 on OpenAlexaff
Nevio Cimolai

Bibliographic record

VenueClinical Infectious Diseases · 2006
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsUniversity of British Columbia
FundersNational University of Ireland
KeywordsEscherichia coliMicrobiologyMedicineShiga toxinToxinShiga-like toxinVirologyBiology

Abstract

fetched live from OpenAlex

To the Editor—The findings of Bennish et al. [1], with regard to a reduction in the frequency of hemolytic uremic syndrome (HUS) among patients who received appropriate antibiotics for Shigella dysenteriae infection, should resurrect interest in designing a prospective antibiotic treatment trial for enterohemorraghic Escherichia coli (EHEC) infection. As the authors detail, Shiga toxin is analogous to one of the common Shiga-like toxins, which are produced by EHEC. Furthermore, there is considerable phylogenetic similarity between E. coli and Shigella species. Both of these factors enhance the theoretical potential that EHEC infections could be benefited by similar treatments, including antimicrobial chemotherapy. As I have previously suggested [2], the choice of antibiotic is critical, because previous studies have shown that, despite in vitro susceptibility, only certain antibiotics are effective for shigellosis [3]. Wong et al. [4] proposed that antibiotics may be a risk factor for progression to HUS during EHEC infection, but the stratification and categorization of antibiotic use would not be consistent with shigellosis treatment trials. A recent meta-analysis from Safdar et al. [5] did not find an increased risk of HUS in EHEC infection. Data from Bell et al. [6] and Proulx et al. [7] also provide similar findings. The latter studies complement those by my colleagues and me [8], in which the safety of certain antibiotics for treatment of E. coli O157:H7 infection was proposed. In other research, we found a possible protective effect of particular antibiotics [9, 10]. Although these studies are not definitive with regard to safety and protection, they, along with the supportive findings of Bennish et al. [1], provide plenty of fuel for hypothesis testing. It is justifiable to propose that an advance in treatment is possible. Caution in the use of antimicrobial chemotherapy is justified, but such caution should not jeopardize the execution of prospective, randomized treatment trials. A choice of ampicillin for the therapeutic trial would be preferable, given the theoretical risk of complicating an evolving nephropathy with a relatively insoluble sulphonamide combination. The treatment group would ideally include children of young age who are at greater risk for progression to HUS and children who are seen early in the course of the illness. The randomized design would necessarily include a preconceived number of patients to achieve sufficient power, but the randomized code and the interim results could be available to an independent oversight group, which would ensure that the study be terminated if preliminary data showed an obvious trend towards adverse effects of antibiotic treatment. The time has come to move forward. Potential conflicts of interest. N.C.: no conflicts.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.350
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2006
Admission routes1
Has abstractno

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