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Record W4245349926 · doi:10.18433/jpps30085

Conference 2018: Translating Innovative Technology to Patient Care. An international symposium held jointly by CSPS, CSPT, and CC-CRS, May 22-25, 2018, Toronto, ON, Canada

2018· article· en· W4245349926 on OpenAlexfundvenueaboutno aff
Canadian Society for Pharmaceutical Sci.

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsnot available
FundersCanada First Research Excellence FundNatural Sciences and Engineering Research Council of CanadaIcahn School of Medicine at Mount SinaiQueen's UniversityUmm Al-Qura UniversityUniversity of Toronto
KeywordsPillarPharmacogenomicsSession (web analytics)MedicineLibrary sciencePharmacologyEngineeringBusinessComputer science

Abstract

fetched live from OpenAlex

Plenaries and Special Presentations:- Mary Haak-Frendscho, Blueline Bioscience & Versant Ventures: The View From Here: Innovation Canadian Style- David Juurlink, Sunnybrook Health Sciences Centre: The North American Opioid Crisis from 30,000 Feet- Tak Mak, Ontario Cancer Institute, Princess Margaret Hospital, Toronto: The Fourth Pillar of Cancer Treatment: It Takes a Village - Richard Weinshilboum, Mayo Clinic: Pharmacogenomics: Clinical Implementation and Future Challenges- Gordon Amidon, University of Michigan: Don’t Throw the BA/BE out with the Bathwater: (Mechanistic Oral BE) CSPS Lifetime Achievement Award: Presentation & Lecture.Conference Sessions:Special Session: Innovation and Management of Modern Pharmaceuticals1. Opioid Crisis2. Regulatory Reforms3. Crossing Biological Membranes4. Knowledge Translation - From Real World Evidence to Canadian Healthcare Needs5. Pharmaceutical Potential of Stem Cell & CRISPR-Mediated Gene Modifications6. Immuno-Oncology7. The Gut Microbiome as a Novel herapeutic Target8. Practical Pharmacology: Case Studies9. Translational Medicine10. Cannabinoids11. Innovative Biomaterials for Drug Delivery12. Pharmacogenomic Implementation13. Drug Therapy in 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.409
Teacher spread0.359 · 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

Citations0
Published2018
Admission routes3
Has abstractyes

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