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Record W2343514415 · doi:10.1039/9781849733496-00290

Anthelmintic Discovery for Human Infections

2011· book-chapter· en· W2343514415 on OpenAlexaff
Timothy G. Geary, Noe͏̈lle Gauvry

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsContext (archaeology)Neglected tropical diseasesIdentification (biology)AnthelminticMedicineIntensive care medicineRisk analysis (engineering)BiologyDiseaseVeterinary medicinePathologyEcology

Abstract

fetched live from OpenAlex

Recognition that neglected tropical diseases due to human helminth infection contribute greatly to the cycle of poverty that plagues poor regions of the world has spurred recent investment in the identification of new and better drugs to control or eliminate these pathogens. None of the anthelmintics used in humans can be considered ‘optimal’, especially in the context of mass drug administration, although their deployment has led to undeniable gains in controlling the pathology and transmission of these infections. A few drugs that have been developed and registered for uses in veterinary medicine or for other human infections may have applications for helminthiases, but new drugs are still needed. Early discovery pipelines for these diseases are not robust, and renewed efforts at lead identification and development are necessary for sustainable control and potential elimination of these pathogens.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.915
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.003

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 head0.038
GPT teacher head0.301
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2011
Admission routes1
Has abstractyes

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