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Įžanginis žodis dedikuotas tarptautinei konferencijai “From bench to bed: challenges in Cancer care“

2013· article· en· W1859917223 on OpenAlexaboutno aff
Narimantas Evaldas Samalavičius

Bibliographic record

VenueLietuvos chirurgija · 2013
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRadiation oncologyPrivilege (computing)OncologyInternal medicinePolitical scienceRadiation therapyLaw

Abstract

fetched live from OpenAlex

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

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.033
GPT teacher head0.356
Teacher spread0.323 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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