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Record W2063107371 · doi:10.1289/ehp.1206316

Human Health Risk Assessment (HHRA) for Environmental Development and Transfer of Antibiotic Resistance

2013· review· en· W2063107371 on OpenAlexaffabout
Nicholas J. Ashbolt, Alejandro Amézquita, Thomas Backhaus, Peter Borriello, Kristian K. Brandt, Peter Collignon, Anja Coors, Rita Finley, William H. Gaze, Thomas Heberer, John R. Lawrence, D. G. Joakim Larsson, Scott A. McEwen, James J. Ryan, Jens Schönfeld, P. Silley, Jason Snape, Christel Van den Eede, Edward Topp

Bibliographic record

VenueEnvironmental Health Perspectives · 2013
Typereview
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of GuelphEnvironment and Climate Change CanadaPublic Health Agency of Canada
Fundersnot available
KeywordsRisk assessmentAntibiotic resistanceEnvironmental healthHuman healthEnvironmental risk assessmentAntibioticsMedicineBiologyMicrobiologyComputer science

Abstract

fetched live from OpenAlex

Abstract Background: Only recently has the environment been clearly implicated in the risk of antibiotic resistance to clinical outcome, but to date there have been few documented approaches to formally assess these risks. Objective: We examined possible approaches and sought to identify research needs to enable human health risk assessments (HHRA) that focus on the role of the environment in the failure of antibiotic treatment caused by antibiotic-resistant pathogens. Methods: The authors participated in a workshop held 4–8 March 2012 in Québec, Canada, to define the scope and objectives of an environmental assessment of antibiotic-resistance risks to human health. We focused on key elements of environmental-resistance-development “hot spots,” exposure assessment (unrelated to food), and dose response to characterize risks that may improve antibiotic-resistance management options. Discussion: Various novel aspects to traditional risk assessments were identified to enable an assessment of environmental antibiotic resistance. These include a) accounting for an added selective pressure on the environmental resistome that, over time, allows for development of antibiotic-resistant bacteria (ARB); b) identifying and describing rates of horizontal gene transfer (HGT) in the relevant environmental “hot spot” compartments; and c) modifying traditional dose–response approaches to address doses of ARB for various health outcomes and pathways. Conclusions: We propose that environmental aspects of antibiotic-resistance development be included in the processes of any HHRA addressing ARB. Because of limited available data, a multicriteria decision analysis approach would be a useful way to undertake an HHRA of environmental antibiotic resistance that informs risk managers. Citation: Ashbolt NJ, Amézquita A, Backhaus T, Borriello P, Brandt KK, Collignon P, Coors A, Finley R, Gaze WH, Heberer T, Lawrence JR, Larsson DG, McEwen SA, Ryan JJ, Schönfeld J, Silley P, Snape JR, Van den Eede C, Topp E. 2013. Human health risk assessment (HHRA) for environmental development and transfer of antibiotic resistance. Environ Health Perspect 121:993–1001; http://dx.doi.org/10.1289/ehp.1206316

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)0.071
metaresearch head score (Gemma)0.074
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.071
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.074
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0050.003
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0030.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.057
GPT teacher head0.388
Teacher spread0.331 · 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
GenreReview

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

Citations661
Published2013
Admission routes2
Has abstractyes

Explore more

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