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Record W2016350863 · doi:10.2217/dmt.10.5

Improving access to insulin: what can be done?

2011· article· en· W2016350863 on OpenAlexfundno aff
David Beran

Bibliographic record

VenueDiabetes Management · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsMedicineInsulinInsulin degludecInternal medicineIntensive care medicineEndocrinologyHypoglycemiaInsulin glargine

Abstract

fetched live from OpenAlex

SUMMARY A third of the world’s population currently has no guaranteed access to essential medicines. Attention has focused on the issue of access to medicines for HIV/AIDS, TB and malaria, but not on access to essential medicines for noncommunicable diseases, including insulin. Insulin has been widely available in the Western world since its discovery in 1921, but in resource-poor settings access to insulin is still problematic due to international and national barriers to access. Solutions proposed have mainly focused on increasing availability and lowering cost. However, insulin alone is not enough for proper diabetes care. In order to improve the lives of people with diabetes, access to medicines needs to be addressed in parallel to creating a health system able to manage all aspects of diabetes care.

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)0.013
metaresearch head score (Gemma)0.059
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.059
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0070.007
Open science0.0020.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0260.002

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 head0.097
GPT teacher head0.273
Teacher spread0.176 · 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
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

Citations21
Published2011
Admission routes1
Has abstractyes

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