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Record W2000923166 · doi:10.1186/1741-7007-8-123

Q&A: Antibiotic resistance: where does it come from and what can we do about it?

2010· article· en· W2000923166 on OpenAlexafffund
Gerard D. Wright

Bibliographic record

VenueBMC Biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsBiologyAntibiotic resistanceAntibioticsResistance (ecology)MicrobiologyGeneticsEcology

Abstract

fetched live from OpenAlex

Yes. Historically, the discovery of the sulfa drugs in the 1930s and the subsequent development of penicillin during World War II ushered in a new era in the treatment of infectious diseases. Infections that were common causes of death and disease in the pre-antibiotic era -rheumatic fever, syphilis, cellulitis and bacterial pneumonia -became treatable, and over the next 20 years most of the classes of antibiotics that find clinical use today were discovered and changed medicine in a profound way. The availability of antibiotics enabled revolu tionary medical interventions such as cancer chemo therapy, organ transplants and essentially all major invasive surgeries from joint replacements to coronary bypass. Antibiotics, though, are unique among drugs in that their use precipitates their obsolescence. Paradoxically, these cures select for organisms that can evade them, fueling an arms race between microbes, clinicians and drug discoverers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score0.986

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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

Citations184
Published2010
Admission routes2
Has abstractyes

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