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Record W1829426509 · doi:10.1002/pbc.25049

Regulatory and logistical issues influencing access to antineoplastic and supportive care medications for children with cancer in developing countries

2014· article· en· W1829426509 on OpenAlexaff
John Wiernikowski, Stuart MacLeod

Bibliographic record

VenuePediatric Blood & Cancer · 2014
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsUniversity of British ColumbiaMcMaster UniversityChild and Family Research InstituteMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineEconomic shortageBlood cancerDeveloping countryLow and middle income countriesPediatric cancerDeveloped countryCommodityAccess to medicinesIntensive care medicineGlobal healthCancerEconomic growthEnvironmental healthNursingBusinessPublic healthPopulationFinance

Abstract

fetched live from OpenAlex

Globally there are numerous impediments, both logistical, regulatory and more recently global drug shortages, hindering pediatric access to therapeutic drugs of all types. Efforts to reduce barriers are ongoing and are especially important in low and middle income countries and for children requiring treatment of conditions such as those encountered in pediatric oncology characterized by the risk of life threatening treatment failures. Progress has been made through the efforts of the World Health Organization and regulators in the US and Europe to encourage the development of therapeutic agents for use in pediatrics and measures taken have fostered the availability of stronger pediatric data to guide therapeutic decisions. Nonetheless, pharmaceuticals remain a global commodity, subject to regulation by the World Trade Organization and this has often had detrimental effects in low and middle income countries. This article emphasizes the need for closer international collaboration to address the barriers currently impeding access to antineoplastic and supportive care medicines for children.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.423

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)0.0000.000

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 head0.028
GPT teacher head0.382
Teacher spread0.354 · 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 designObservational
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

Citations9
Published2014
Admission routes1
Has abstractyes

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