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Record W2083863469 · doi:10.1080/17441690903456274

University leadership for innovation in global health and HIV/AIDS diagnostics

2010· article· en· W2083863469 on OpenAlexaff
Kara Palamountain, Kearsley A. Stewart, Alexander Krauß, David M. Kelso, Daniel Diermeier

Bibliographic record

VenueGlobal Public Health · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsKellogg's (Canada)
FundersNorthwestern UniversityBill and Melinda Gates Foundation
KeywordsIncentiveProduct (mathematics)Resource (disambiguation)Health careTest (biology)Medical equipmentBusinessHuman immunodeficiency virus (HIV)Public relationsNew product developmentMarketingMedicineEconomic growthPolitical scienceEconomicsNursingFamily medicineComputer science

Abstract

fetched live from OpenAlex

Medical products used in the developed world often fail to adequately serve resource-limited settings where electricity, transportation and health care workers are not readily available. We suggest that the problem is not only a lack of coordinated financial resources to purchase existing medical products, but also a lack of products that are specifically designed for resource-limited settings. While donor organisations with a focus on global health are increasingly willing to bear the additional financial risk for the research and development of such high-impact medical products, corporations are still reluctant to take their best scientists and engineers away from more commercially attractive projects. Universities, on the other hand, given their teaching and research missions, are well positioned to engage in such high-risk development projects. A group of biomedical, engineering, business and social science researchers at Northwestern University (NU) propose a creative model to address significant social and health needs. The team's initial product focus is a rapid test for diagnosing infants with HIV. The NU model aligns the incentives and expertise of industry, donors and academia to innovate medical products, such as the infant HIV diagnostic test, for resource-limited settings.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.094
GPT teacher head0.389
Teacher spread0.296 · 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 designNot applicable
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

Citations7
Published2010
Admission routes1
Has abstractyes

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