Rhinology Future Debates, an EUFOREA Report
Bibliographic record
Abstract
The first Rhinology Future Debates was held in Brussels in December 2016, organized by EUFOREA (European Forum for Research and Education in Allergy and Airways diseases). The purpose of these debates is to bring novel developments in the field of Rhinology to the attention of the medical, paramedical and patient community, in a highly credible and balanced context. For the first time in Rhinology, a peer to peer scientific exchange with key experts in the field of rhinology and key medical colleagues from leading industries let to a brainstorming and discussion event on a number of hot issues in Rhinology. Novel developments are presented by key experts from industry and/or key thought leaders in Rhinology, and then followed by a lively debate on the potential positioning of new developments in care pathways, the strengths and weaknesses of the novel development(s), and comparisons with existing and/or competing products, devices, and/or molecules. As all debates are recorded and distributed on-line with limited editing (www.rhinology-future.com), EUFOREA aims at maximizing the education of the target groups on novel developments, allowing a critical appraisal of the future and a more rapid implementation of promising novel tools, techniques and/or molecules in clinical practise in Europe. The next Rhinology Future debate will be held in Brussels in December 2017.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 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".