Bibliographic record
Abstract
The Emirates Oncology Conference (EOC), now in its 11th year, continues to highlight important developments in the fields of cancer prevention and treatment.The Abu Dhabi Health Services -SEHA, which is dedicated to offering the UAE top-tier healthcare and ongoing medical education to keep the public informed and ensure that medical experts remain up to date in their fields, is hosting the conference.The EOC featured significant sessions covering a wide range of topics with globally renowned speakers who kept the audience interested with their illuminating lectures and visual presentations.Breast cancer, hematological malignancies, palliative care, lung cancer, radiation oncology, pediatric oncology, genitourinary, gastrointestinal, and neuro-oncology were some of the topics covered.Over the course of the 3 days, there were about 2600 attendees including physicians, surgeons, researchers, healthcare professionals, and industry representatives from over 40 nations, of which 96% were from the UAE and 4% traveled from the USA, Europe, Middle East, and GCC.Approximately 150 speakers were hosted by EOC; speakers came from the USA, UK, Italy, Spain, France, Germany, Belgium, India, Pakistan, Philippines, Malaysia, Canada, and many regional nations such as Jordan, KSA, Oman, Bahrain, Lebanon, Egypt, and the UAE.In addition, more than 2 dozen abstracts were presented as oral and poster presentations, and the top 14 were chosen to be published in a medical journal as a result of EOC 2023.Selected abstracts are included herein.We are grateful to all members of the scientific and medical community, our organizing team, speakers, delegates and sponsors for their time, efforts, and contributions to making EOC one of the most important scientific events of the year.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.171 | 0.074 |
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".