MétaCan
Menu
Back to cohort
Record W2313184717 · doi:10.3332/ecancer.2016.630

Highlights of Children with Cancer UK’s Workshop on Drug Delivery in Paediatric Brain Tumours

2016· article· en· W2313184717 on OpenAlexaff
Audrey Nailor, David Walker, Thomas S. Jacques, Kathy Warren, Henry Brem, Pamela Kearns, John Greenwood, Jeffrey Penny, Geoffrey J. Pilkington, Ángel M. Carcaboso, Gudrun Fleischhack, Donald Macarthur, Irene Slavc, Lisethe Meijer, Steven S. Gill, Stephen P. Lowis, Dannis G. van Vuurden, Monica S. Pearl, Steven C. Clifford, A. Sorana Morrissy, Delyan P. Ivanov, Kévin Beccaria, Richard J. Gilbertson, Karin Straathof, Jordan J. Green, Stuart Smith, Ruman Rahman, John‐Paul Kilday

Bibliographic record

Venueecancermedicalscience · 2016
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick Children
FundersNational Cancer InstituteEngineering and Physical Sciences Research CouncilBrain Tumour CharityAcademy of Medical SciencesCancer Research UK
KeywordsMedicineEmotiveUnderpinningCancer drugsBrain cancerBlood cancerCancerDrugPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The first Workshop on Drug Delivery in Paediatric Brain Tumours was hosted in London by the charity Children with Cancer UK. The goals of the workshop were to break down the barriers to treating central nervous system (CNS) tumours in children, leading to new collaborations and further innovations in this under-represented and emotive field. These barriers include the physical delivery challenges presented by the blood-brain barrier, the underpinning reasons for the intractability of CNS cancers, and the practical difficulties of delivering cancer treatment to the brains of children. Novel techniques for overcoming these problems were discussed, new models brought forth, and experiences compared.

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 score0.061
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueecancermedicalscienceSame topicGlioma Diagnosis and TreatmentFrench-language works237,207