Bibliographic record
Abstract
It is our great pleasure to welcome the attendees to the Third Asia-Pacific Cholangiocarcinoma conference to Taipei on March 15-16, 2019.Cholangiocarcinoma is an orphan cancer in the West, but an important cancer in Asia that often presents late and has a dismal outcome.The etiology of this cancer varies according to geographic location and this may lead to clinical, genomic and immunologic heterogeneity.Global partnership between key stakeholders will be critical towards increasing our understanding of this cancer and improving the clinical outcome of patients.This event is sponsored by the International Cholangiocarcinoma Research Network (ICRN; https://cholangiocarcinoma.org/internationalcholangiocarcinoma-research-network/).ICRN is a subsidiary of the Cholangiocarcinoma Foundation and is a global network of leading cancer centers that are working together to improve knowledge of this cancer.We are proud to host attendees from several Asian countries, Europe and USA to this important event.This is a multi-disciplinary conference that includes clinical and basic science researchers, clinicians, nurses, patients and their advocates.For the first time, we will be including original research abstracts and poster presentations during this conference.It is our privilege to present these abstracts in Hepatobiliary Surgery and Nutrition (HBSN) and we look forward to a productive partnership with this important journal.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.471 | 0.420 |
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".