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Record W1547543065 · doi:10.18433/j3nk5m

Foresight Scanning: Future Directions of Clinical and Pharmaceutical Research

2009· article· en· W1547543065 on OpenAlexaffvenueabout
Brian C. Foster

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2009
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsHealth Canada
Fundersnot available
KeywordsFutures studiesPharmaceutical sciencesMedicineBusinessComputer sciencePharmacologyArtificial intelligence

Abstract

fetched live from OpenAlex

Foresight Scanning: Future Directions of Clinical and Pharmaceutical Research. Brian C. Foster, Therapeutic Products Directorate, Health Canada, Ottawa, Ontario, Canada ABSTRACT The Canadian Society for Pharmaceutical Sciences Satellite Symposium on Foresight Scanning, May 26 and 27, 2008, Nordegg, Alberta, Canada, focussed on the future directions of clinical and pharmaceutical research. The symposium brought together a group of clinicians, regulatory scientists, researchers and students to examine where clinical, pharmaceutical, and regulatory science might be in 10 to 15 years. Industry, regulatory, analytical, and clinical perspectives were presented and discussed, as well as the impact of exogenous (indirect) and endogenous (direct) change drivers. Unconditional funding was provided by Bayer HealthCare; they had no input on the direction of the meeting or selection of speakers. It was envisioned that the more important endogenous drivers may not be new information or changes in technology, policy, regulation, or health care delivery, but amplification of long-term underlying trends by emergence of new technologies, convergence of existing technologies or new communication and collaboration vehicles such as Web 2.0.

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)0.014
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.877
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.002
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 head0.288
GPT teacher head0.579
Teacher spread0.291 · 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.

Study designOther design
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

Citations5
Published2009
Admission routes3
Has abstractyes

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