Foresight Scanning: Future Directions of Clinical and Pharmaceutical Research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".