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Record W1955301764 · doi:10.4269/ajtmh.2009.80.1012

Increasing Fluoroquinolone Resistance in Salmonella typhi in Ontario, 2002–2007

2009· article· en· W1955301764 on OpenAlexaffabout
Shaun K. Morris, Susan E. Richardson, Laura Sauvé, Elizabeth Ford-Jones, Frances Jamieson

Bibliographic record

VenueAmerican Journal of Tropical Medicine and Hygiene · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsNalidixic acidTyphoid feverCiprofloxacinSalmonella typhiCeftriaxoneCefotaximeMedicineAmpicillinTrimethoprimChloramphenicolDrug resistanceSalmonellaCephalosporinAntibioticsSulfamethoxazoleInternal medicineMicrobiologyVeterinary medicineBiologyVirologyBacteria

Abstract

fetched live from OpenAlex

We reviewed the antibiotic susceptibility patterns of all isolates of Salmonella typhi in Ontario, Canada from January 2002 to December 2007. We identified a total of 381 unique cases over the 5-year period (50-73 cases per year). Of the 381 cases, 171 were female, 164 were male, and no gender was identified for 33 cases. Age of the patients ranged from less than 1 to 102 years of age (median age of 20 years). Although resistance patterns for ampicillin, trimethoprim-sulfamethoxazole, third generation cephalosporins (cefotaxime until May 2005 and ceftriaxone from June 2005 to present), and chloramphenicol remained stable, nalidixic acid resistance rose sharply between 2003 and 2005 and has remained at approximately 80% of isolates since 2005. The significant and sustained increase in nalidixic acid-resistant S. typhi suggests that ciprofloxacin should no longer be used as the drug of choice for the empiric treatment of typhoid fever in Ontario.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.251
Teacher spread0.230 · 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 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

Citations8
Published2009
Admission routes2
Has abstractyes

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