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Record W2128245173 · doi:10.5210/ojphi.v7i1.5734

CIPARS: A One-Health Approach to Antimicrobial Resistance Surveillance

2015· article· en· W2128245173 on OpenAlexaffabout
Anne Deckert, Agnes Agunos, Brent P. Avery, Carolee A. Carson, Danielle Daignault, Rita Finley, Sheryl Gow, David Léger, Michael R. Mulvey, E. Jane Parmley, Richard J. Reid‐Smith, Rebecca Irwin

Bibliographic record

VenueOnline Journal of Public Health Informatics · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsPublic healthPublic health surveillanceAntibiotic resistanceAgency (philosophy)StakeholderAntimicrobialPsychological interventionAction (physics)MedicineHealth surveillanceEnvironmental healthPublic relationsPolitical scienceNursingBiologyMicrobiologySociology

Abstract

fetched live from OpenAlex

The objective of the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) is to provide a unified approach to monitor national trends in antimicrobial resistance (AMR) and antimicrobial use (AMU) in humans and animals and to facilitate the assessment of the public health impact of antimicrobial use. CIPARS is a combination of passive and active surveillance that is coordinated by the Public Health Agency of Canada (PHAC) but is based on extensive collaborations. Stakeholder engagement has led to action based on surveillance results. This One-Health approach provides a holistic understanding of AMR in Canada and enables the evaluation of interventions.

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)0.028
metaresearch head score (Gemma)0.025
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.456
Threshold uncertainty score0.906

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.006
Science and technology studies0.0060.007
Scholarly communication0.0120.005
Open science0.0060.014
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0100.002

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 head0.105
GPT teacher head0.326
Teacher spread0.221 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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