Abstracts of the 3<sup>rd</sup> International Conference on Cancer Cachexia, September 23–25, 2016 in Washington, DC, USA
Bibliographic record
Abstract
The 3rd International Conference on Cancer Cachexia titled, "Advancing Molecular Mechanisms and Therapies" took place on September 23-25, 2016, in Washington, DC.This meeting followed the 1st conference in 2012 in Boston, USA, and the 2nd in 2014 in Montreal, Canada.The goal for the 3rd meeting was to continue to advance our understanding of the basis of cancer-induced cachexia by discussing new topics and unpublished data.In addition, we placed greater emphasis in this meeting on therapies and the education of clinical trial design and outcome measures.The meeting included new sessions to discuss cross-talk pathways between tissues, new animal models of cancer cachexia, and interpretations of large data sets.In addition, the meeting incorporated a half-day session on the design of cancer cachexia clinical trials, an event that was well attended and led to engaging conversations among attendees.Highlights from the conference included our Keynote Speaker, Dr Vickie Baracos from the University of Alberta, a presentation from a patient advocate, Mr David Kochanczyk, and a special lecture from Dr Douglas Lowy, Director of the National Cancer Institute, USA.We also presented our personal reflections to remember our close colleague, Dr Ken Fearon who truly was a maverick in the field.Below represent some of the abstracts presented at the conference supporting both oral and poster presentations.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.278 | 0.137 |
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".