Įžanginis žodis dedikuotas tarptautinei konferencijai “From bench to bed: challenges in Cancer care“
Bibliographic record
Abstract
Dear colleagues,It is my great privilege and pleasure to welcome you to the international conference “From bench to bed: challenges in cancer care” devoted to a very broad number of topics in oncology. It is of utmost importance to stress that during this event we are celebrating the 80th anniversary of the Lithuanian Society of Oncology – just to stress how deep and long are the traditions of oncology in our country. I am very pleased to note that this event attracted as many as 25 international speakers from Russia, Belarus, Ukraine, Poland, Italy, Belgium, Switzerland, Germany, the United Kingdom, Austria, France, Canada, the USA, Taiwan.It has been quite a tradition that during the last years the Oncology Institute of Vilnius University has been organizing meetings dealing with many specialities and subspecialties in oncology, such as diagnostics, surgery, medical oncology, psychooncology, etc. This time, we decided to cover most of the important issues in this field, thus allowing a very large number of different medical professionals to benefit from this magnificient arrangement.Needless to remind you that in recent years a very big step has been made in oncology worldwide: among a number of aspects, cancer diagnosis has been armed with new modalities we could only dream of several decades ago; minimally invasive surgical techniques, including robotics, became an unseperable part of surgical oncology; radiation therapy crossed the borders we ever though we had, and today’s cancer therapy enables us to approach cancer with different targeted drugs in the light of understanding the newly discovered mechanisms. However, there is so much yet to do: some oncological diseases are still as fatal as they were, modern treatments are a huge bondage to our societies due to dramatically increasing costs, stressing the further need of cancer prevention and early diagnosis.I do hope that this unique place in our tiny Lithuania, called Druskininkai, in the light of the soft autumnal sun will help us to create a truly scientific atmosphere; the meeting itself will be a start for new ideas, new thoughts, and new steps in our research and practice. At the same time, the social program will offer us a true relax and a succesfull return to our daily activites next week.Yours truly –Prof. NARIMANTAS EVALDAS SAMALAVICIUSDirector of the Institute of Oncology, Vilnius University
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".