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Record W2547467223 · doi:10.1080/14737167.2017.1256775

Outcome of the second Medicines Utilisation Research in Africa Group meeting to promote sustainable and appropriate medicine use in Africa

2016· article· en· W2547467223 on OpenAlexaff
Amos Massele, Johanita R. Burger, Francis Kalemeera, Mary Jande, Thatayaone Didimalang, Aubrey Chichonyi Kalungia, Kidwell Matshotyana, Michael R. Law, Brighid Malone, Olayinka O. Ogunleye, Margaret Oluka, Bene D. Anand Paramadhas, Godfrey Mutashambara Rwegerera, Sekesai Mtapuri‐Zinyowera, Brian Godman

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAlternative medicineFamily medicinePolitical scienceTraditional medicine

Abstract

fetched live from OpenAlex

The second Medicines Utilization Research in Africa (MURIA) group workshop and symposium again brought researchers together from across Africa to improve their knowledge of drug utilization (DU) methodologies and exchange ideas to further progress DU research in Africa. This built on extensive activities from the first conference including workshops and multiple publications. Anti-infectives were again the principal theme for the 2016 symposium following the workshops. This included presentations regarding strategies to improve antibiotic utilization among African countries, such as point-prevalence studies, as well as potential ways to reduce self-purchasing of antibiotics. There were also presentations on antiretrovirals including renal function and the impact of policy changes. Concerns with adherence in chronic treatments as well as drug-drug interactions and their implications were also discussed. The deliberations resulted in a number of agreed activities including joint publications before the next MURIA conference in Namibia in 2017.

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)0.020
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 score0.903
Threshold uncertainty score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.189
GPT teacher head0.496
Teacher spread0.307 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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