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Record W2004411700 · doi:10.1016/s2214-109x(14)70318-3

Research priorities for elimination of visceral leishmaniasis

2014· article· en· W2004411700 on OpenAlexaff
Greg Matlashewski, Byron Arana, Axel Kroeger, Ahmed Be-Nazir, Dinesh Mondal, Shan Golam Nabi, Megha Raj Banjara, Murari Lal Das, Baburam Marasini, Pradeep Das, Graham F. Medley, Abhay R. Satoskar, Hira L. Nakhasi, Daniel Argaw, John C. Reeder, Piero Olliaro

Bibliographic record

VenueThe Lancet Global Health · 2014
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsMcGill University
FundersWorld Health Organization
KeywordsVisceral leishmaniasisNeglected tropical diseasesLeishmaniasisPsychological interventionTropical diseaseGovernment (linguistics)MedicineDeveloping countryIndoor residual sprayingEconomic growthDiseaseEnvironmental healthImmunologyMalariaPathologyEconomics

Abstract

fetched live from OpenAlex

Now is a good time to reconsider research priorities as 2015 approaches, the target date originally set for elimination of visceral leishmaniasis. Visceral leishmaniasis is one of the most deadly parasitic diseases and disproportionately affects the poorest and most vulnerable populations. An estimated 200 000–400 000 people contract visceral leishmaniasis every year in developing countries. Spread by sandflies, visceral leishmaniasis can be fought with existing interventions, including treatment and vector control, but, similarly to every other human parasitic disease, no effective vaccine exists.

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)0.062
metaresearch head score (Gemma)0.058
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.058
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.003
Science and technology studies0.0040.006
Scholarly communication0.0150.017
Open science0.0060.008
Research integrity0.0170.025
Insufficient payload (model declined to judge)0.0320.015

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 head0.135
GPT teacher head0.485
Teacher spread0.350 · 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

Citations33
Published2014
Admission routes1
Has abstractyes

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